parent_paper_title
stringclasses 63
values | parent_paper_arxiv_id
stringclasses 63
values | citation_shorthand
stringlengths 2
56
| raw_citation_text
stringlengths 9
63
| cited_paper_title
stringlengths 5
161
| cited_paper_arxiv_link
stringlengths 32
37
⌀ | cited_paper_abstract
stringlengths 406
1.92k
⌀ | has_metadata
bool 1
class | is_arxiv_paper
bool 2
classes | bib_paper_authors
stringlengths 2
2.44k
⌀ | bib_paper_year
float64 1.97k
2.03k
⌀ | bib_paper_month
stringclasses 16
values | bib_paper_url
stringlengths 20
116
⌀ | bib_paper_doi
stringclasses 269
values | bib_paper_journal
stringlengths 3
148
⌀ | original_title
stringlengths 5
161
| search_res_title
stringlengths 4
122
| search_res_url
stringlengths 22
267
| search_res_content
stringlengths 19
1.92k
|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Efficient 3D Brain Tumor Segmentation with Axial-Coronal-Sagittal
Embedding
|
2506.00434v1
|
ronneberger_unet_miccai_2015
|
\cite{ronneberger_unet_miccai_2015}
|
U-net: Convolutional networks for biomedical image segmentation
| null | null | true | false |
Ronneberger, Olaf and Fischer, Philipp and Brox, Thomas
| 2,015 | null | null | null | null |
U-net: Convolutional networks for biomedical image segmentation
|
U-Net: Convolutional Networks for Biomedical Image Segmentation
|
http://arxiv.org/pdf/1505.04597v1
|
There is large consent that successful training of deep networks requires
many thousand annotated training samples. In this paper, we present a network
and training strategy that relies on the strong use of data augmentation to use
the available annotated samples more efficiently. The architecture consists of
a contracting path to capture context and a symmetric expanding path that
enables precise localization. We show that such a network can be trained
end-to-end from very few images and outperforms the prior best method (a
sliding-window convolutional network) on the ISBI challenge for segmentation of
neuronal structures in electron microscopic stacks. Using the same network
trained on transmitted light microscopy images (phase contrast and DIC) we won
the ISBI cell tracking challenge 2015 in these categories by a large margin.
Moreover, the network is fast. Segmentation of a 512x512 image takes less than
a second on a recent GPU. The full implementation (based on Caffe) and the
trained networks are available at
http://lmb.informatik.uni-freiburg.de/people/ronneber/u-net .
|
Efficient 3D Brain Tumor Segmentation with Axial-Coronal-Sagittal
Embedding
|
2506.00434v1
|
menze_tmi_2015
|
\cite{menze_tmi_2015}
|
The {Multimodal} {Brain} {Tumor} {Image} {Segmentation} {Benchmark} ({BRATS})
| null | null | true | false |
Menze, Bjoern H and Jakab, Andras and Bauer, Stefan and Kalpathy-Cramer, Jayashree and Farahani, Keyvan and Kirby, Justin and Burren, Yuliya and Porz, Nicole and Slotboom, Johannes and Wiest, Roland and others
| 2,015 | null | null | null |
IEEE TMI
|
The {Multimodal} {Brain} {Tumor} {Image} {Segmentation} {Benchmark} ({BRATS})
|
The Multimodal Brain Tumor Image Segmentation Benchmark ...
|
https://pmc.ncbi.nlm.nih.gov/articles/PMC4833122/
|
The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) - PMC The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) Find articles by Thomas J Taylor Find articles by Nicholas J Tustison [DOI00671-8)] [PMC free article] [PubMed] [Google Scholar00671-8&)] [DOI] [PMC free article] [PubMed] [Google Scholar] [DOI] [PMC free article] [PubMed] [Google Scholar] [DOI] [PMC free article] [PubMed] [Google Scholar] [DOI] [PMC free article] [PubMed] [Google Scholar] [DOI] [PMC free article] [PubMed] [Google Scholar] [DOI] [PMC free article] [PubMed] [Google Scholar] [DOI] [PMC free article] [PubMed] [Google Scholar] [DOI] [PMC free article] [PubMed] [Google Scholar] [DOI] [PMC free article] [PubMed] [Google Scholar] [DOI] [PMC free article] [PubMed] [Google Scholar] [DOI] [PMC free article] [PubMed] [Google Scholar] [DOI1522-2594(200004)43:4%3C589::aid-mrm14%3E3.0.co;2-2)] [PubMed] [Google Scholar%20and%20B(0)%20variations%20in%20quantitative%20T2%20measurements%20using%20MRI&author=J%20Sled&author=G%20Pike&volume=43&issue=4&publication_year=2000&pages=589-593&pmid=10748435&doi=10.1002/(sici)1522-2594(200004)43:4%3C589::aid-mrm14%3E3.0.co;2-2&)]
|
Efficient 3D Brain Tumor Segmentation with Axial-Coronal-Sagittal
Embedding
|
2506.00434v1
|
bakas_arxiv_2019
|
\cite{bakas_arxiv_2019}
|
Identifying the Best Machine Learning Algorithms for Brain Tumor
Segmentation, Progression Assessment, and Overall Survival Prediction in the
BRATS Challenge
|
http://arxiv.org/abs/1811.02629v3
|
Gliomas are the most common primary brain malignancies, with different
degrees of aggressiveness, variable prognosis and various heterogeneous
histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic
core, active and non-enhancing core. This intrinsic heterogeneity is also
portrayed in their radio-phenotype, as their sub-regions are depicted by
varying intensity profiles disseminated across multi-parametric magnetic
resonance imaging (mpMRI) scans, reflecting varying biological properties.
Their heterogeneous shape, extent, and location are some of the factors that
make these tumors difficult to resect, and in some cases inoperable. The amount
of resected tumor is a factor also considered in longitudinal scans, when
evaluating the apparent tumor for potential diagnosis of progression.
Furthermore, there is mounting evidence that accurate segmentation of the
various tumor sub-regions can offer the basis for quantitative image analysis
towards prediction of patient overall survival. This study assesses the
state-of-the-art machine learning (ML) methods used for brain tumor image
analysis in mpMRI scans, during the last seven instances of the International
Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we
focus on i) evaluating segmentations of the various glioma sub-regions in
pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue
of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO
criteria, and iii) predicting the overall survival from pre-operative mpMRI
scans of patients that underwent gross total resection. Finally, we investigate
the challenge of identifying the best ML algorithms for each of these tasks,
considering that apart from being diverse on each instance of the challenge,
the multi-institutional mpMRI BraTS dataset has also been a continuously
evolving/growing dataset.
| true | true |
Bakas, Spyridon and Reyes, Mauricio and Jakab, Andras and Bauer, Stefan and Rempfler, Markus and Crimi, Alessandro and Shinohara, Russell Takeshi and Berger, Christoph and Ha, Sung Min and Rozycki, Martin and others
| 2,018 | null | null | null |
arXiv preprint arXiv:1811.02629
|
Identifying the Best Machine Learning Algorithms for Brain Tumor
Segmentation, Progression Assessment, and Overall Survival Prediction in the
BRATS Challenge
|
Identifying the Best Machine Learning Algorithms for Brain Tumor ...
|
https://arxiv.org/abs/1811.02629
|
View a PDF of the paper titled Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge, by Spyridon Bakas and 426 other authors View a PDF of the paper titled Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge, by Spyridon Bakas and 426 other authors
|
Efficient 3D Brain Tumor Segmentation with Axial-Coronal-Sagittal
Embedding
|
2506.00434v1
|
baid_arxiv_2021
|
\cite{baid_arxiv_2021}
|
The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation
and Radiogenomic Classification
|
http://arxiv.org/abs/2107.02314v2
|
The BraTS 2021 challenge celebrates its 10th anniversary and is jointly
organized by the Radiological Society of North America (RSNA), the American
Society of Neuroradiology (ASNR), and the Medical Image Computing and Computer
Assisted Interventions (MICCAI) society. Since its inception, BraTS has been
focusing on being a common benchmarking venue for brain glioma segmentation
algorithms, with well-curated multi-institutional multi-parametric magnetic
resonance imaging (mpMRI) data. Gliomas are the most common primary
malignancies of the central nervous system, with varying degrees of
aggressiveness and prognosis. The RSNA-ASNR-MICCAI BraTS 2021 challenge targets
the evaluation of computational algorithms assessing the same tumor
compartmentalization, as well as the underlying tumor's molecular
characterization, in pre-operative baseline mpMRI data from 2,040 patients.
Specifically, the two tasks that BraTS 2021 focuses on are: a) the segmentation
of the histologically distinct brain tumor sub-regions, and b) the
classification of the tumor's O[6]-methylguanine-DNA methyltransferase (MGMT)
promoter methylation status. The performance evaluation of all participating
algorithms in BraTS 2021 will be conducted through the Sage Bionetworks Synapse
platform (Task 1) and Kaggle (Task 2), concluding in distributing to the top
ranked participants monetary awards of $60,000 collectively.
| true | true |
Baid, Ujjwal and Ghodasara, Satyam and Mohan, Suyash and Bilello, Michel and Calabrese, Evan and Colak, Errol and Farahani, Keyvan and Kalpathy-Cramer, Jayashree and Kitamura, Felipe C and Pati, Sarthak and others
| 2,021 | null | null | null |
arXiv preprint arXiv:2107.02314
|
The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation
and Radiogenomic Classification
|
BraTS-Lighthouse 2025 Challenge - syn64153130 - Wiki
|
https://www.synapse.org/Synapse:syn64153130/wiki/631064
|
[1] U.Baid, et al., The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification, arXiv:2107.02314, 2021.
|
Efficient 3D Brain Tumor Segmentation with Axial-Coronal-Sagittal
Embedding
|
2506.00434v1
|
myronenko_miccai_2019
|
\cite{myronenko_miccai_2019}
|
3D MRI brain tumor segmentation using autoencoder regularization
|
http://arxiv.org/abs/1810.11654v3
|
Automated segmentation of brain tumors from 3D magnetic resonance images
(MRIs) is necessary for the diagnosis, monitoring, and treatment planning of
the disease. Manual delineation practices require anatomical knowledge, are
expensive, time consuming and can be inaccurate due to human error. Here, we
describe a semantic segmentation network for tumor subregion segmentation from
3D MRIs based on encoder-decoder architecture. Due to a limited training
dataset size, a variational auto-encoder branch is added to reconstruct the
input image itself in order to regularize the shared decoder and impose
additional constraints on its layers. The current approach won 1st place in the
BraTS 2018 challenge.
| true | true |
Myronenko, Andriy
| 2,019 | null | null | null | null |
3D MRI brain tumor segmentation using autoencoder regularization
|
3D MRI brain tumor segmentation using autoencoder regularization
|
http://arxiv.org/pdf/1810.11654v3
|
Automated segmentation of brain tumors from 3D magnetic resonance images
(MRIs) is necessary for the diagnosis, monitoring, and treatment planning of
the disease. Manual delineation practices require anatomical knowledge, are
expensive, time consuming and can be inaccurate due to human error. Here, we
describe a semantic segmentation network for tumor subregion segmentation from
3D MRIs based on encoder-decoder architecture. Due to a limited training
dataset size, a variational auto-encoder branch is added to reconstruct the
input image itself in order to regularize the shared decoder and impose
additional constraints on its layers. The current approach won 1st place in the
BraTS 2018 challenge.
|
Efficient 3D Brain Tumor Segmentation with Axial-Coronal-Sagittal
Embedding
|
2506.00434v1
|
jiang_cascaded_unet_miccai_2020
|
\cite{jiang_cascaded_unet_miccai_2020}
|
Two-stage cascaded u-net: 1st place solution to brats challenge 2019 segmentation task
| null | null | true | false |
Jiang, Zeyu and Ding, Changxing and Liu, Minfeng and Tao, Dacheng
| 2,020 | null | null | null | null |
Two-stage cascaded u-net: 1st place solution to brats challenge 2019 segmentation task
|
Two-Stage Cascaded U-Net: 1st Place Solution to BraTS Challenge ...
|
https://www.semanticscholar.org/paper/Two-Stage-Cascaded-U-Net%3A-1st-Place-Solution-to-Jiang-Ding/6eead90d63cc679263ef608121db075b78e03960
|
A novel two-stage cascaded U-Net to segment the substructures of brain tumors from coarse to fine is devised and won the 1st place in the BraTS 2019
|
Efficient 3D Brain Tumor Segmentation with Axial-Coronal-Sagittal
Embedding
|
2506.00434v1
|
isensee_nnunet_miccai_2021
|
\cite{isensee_nnunet_miccai_2021}
|
nnU-Net for Brain Tumor Segmentation
|
http://arxiv.org/abs/2011.00848v1
|
We apply nnU-Net to the segmentation task of the BraTS 2020 challenge. The
unmodified nnU-Net baseline configuration already achieves a respectable
result. By incorporating BraTS-specific modifications regarding postprocessing,
region-based training, a more aggressive data augmentation as well as several
minor modifications to the nnUNet pipeline we are able to improve its
segmentation performance substantially. We furthermore re-implement the BraTS
ranking scheme to determine which of our nnU-Net variants best fits the
requirements imposed by it. Our final ensemble took the first place in the
BraTS 2020 competition with Dice scores of 88.95, 85.06 and 82.03 and HD95
values of 8.498,17.337 and 17.805 for whole tumor, tumor core and enhancing
tumor, respectively.
| true | true |
Isensee, Fabian and J{\"a}ger, Paul F and Full, Peter M and Vollmuth, Philipp and Maier-Hein, Klaus H
| 2,021 | null | null | null | null |
nnU-Net for Brain Tumor Segmentation
|
Brain tumor segmentation with advanced nnU-Net - ScienceDirect.com
|
https://www.sciencedirect.com/science/article/pii/S2772528624000013
|
This paper introduces an extended version of the nnU-Net architecture for brain tumor segmentation, addressing both adult (Glioma) and pediatric tumors.
|
Efficient 3D Brain Tumor Segmentation with Axial-Coronal-Sagittal
Embedding
|
2506.00434v1
|
luu_miccai_2022
|
\cite{luu_miccai_2022}
|
Extending nn-UNet for brain tumor segmentation
|
http://arxiv.org/abs/2112.04653v1
|
Brain tumor segmentation is essential for the diagnosis and prognosis of
patients with gliomas. The brain tumor segmentation challenge has continued to
provide a great source of data to develop automatic algorithms to perform the
task. This paper describes our contribution to the 2021 competition. We
developed our methods based on nn-UNet, the winning entry of last year
competition. We experimented with several modifications, including using a
larger network, replacing batch normalization with group normalization, and
utilizing axial attention in the decoder. Internal 5-fold cross validation as
well as online evaluation from the organizers showed the effectiveness of our
approach, with minor improvement in quantitative metrics when compared to the
baseline. The proposed models won first place in the final ranking on unseen
test data. The codes, pretrained weights, and docker image for the winning
submission are publicly available at
https://github.com/rixez/Brats21_KAIST_MRI_Lab
| true | true |
Luu, Huan Minh and Park, Sung-Hong
| 2,021 | null | null | null | null |
Extending nn-UNet for brain tumor segmentation
|
Extending nn-UNet for Brain Tumor Segmentation
|
https://link.springer.com/chapter/10.1007/978-3-031-09002-8_16
|
by HM Luu · 2021 · Cited by 185 — We extended the nn-UNet framework by using a larger network, replacing batch normalization with group normalization, and using axial attention
|
Efficient 3D Brain Tumor Segmentation with Axial-Coronal-Sagittal
Embedding
|
2506.00434v1
|
zeineldin_miccai_2022
|
\cite{zeineldin_miccai_2022}
|
Multimodal CNN Networks for Brain Tumor Segmentation in MRI: A BraTS
2022 Challenge Solution
|
http://arxiv.org/abs/2212.09310v1
|
Automatic segmentation is essential for the brain tumor diagnosis, disease
prognosis, and follow-up therapy of patients with gliomas. Still, accurate
detection of gliomas and their sub-regions in multimodal MRI is very
challenging due to the variety of scanners and imaging protocols. Over the last
years, the BraTS Challenge has provided a large number of multi-institutional
MRI scans as a benchmark for glioma segmentation algorithms. This paper
describes our contribution to the BraTS 2022 Continuous Evaluation challenge.
We propose a new ensemble of multiple deep learning frameworks namely, DeepSeg,
nnU-Net, and DeepSCAN for automatic glioma boundaries detection in
pre-operative MRI. It is worth noting that our ensemble models took first place
in the final evaluation on the BraTS testing dataset with Dice scores of
0.9294, 0.8788, and 0.8803, and Hausdorf distance of 5.23, 13.54, and 12.05,
for the whole tumor, tumor core, and enhancing tumor, respectively.
Furthermore, the proposed ensemble method ranked first in the final ranking on
another unseen test dataset, namely Sub-Saharan Africa dataset, achieving mean
Dice scores of 0.9737, 0.9593, and 0.9022, and HD95 of 2.66, 1.72, 3.32 for the
whole tumor, tumor core, and enhancing tumor, respectively. The docker image
for the winning submission is publicly available at
(https://hub.docker.com/r/razeineldin/camed22).
| true | true |
Zeineldin, Ramy A and Karar, Mohamed E and Burgert, Oliver and Mathis-Ullrich, Franziska
| 2,022 | null | null | null |
arXiv preprint arXiv:2212.09310
|
Multimodal CNN Networks for Brain Tumor Segmentation in MRI: A BraTS
2022 Challenge Solution
|
Multimodal CNN Networks for Brain Tumor Segmentation in MRI
|
https://link.springer.com/chapter/10.1007/978-3-031-33842-7_11
|
The BraTS challenge is designed to encourage research in the field of medical image segmentation, with a focus on segmenting brain tumors in MRI
|
Efficient 3D Brain Tumor Segmentation with Axial-Coronal-Sagittal
Embedding
|
2506.00434v1
|
isensee_nnunet_nature_2021
|
\cite{isensee_nnunet_nature_2021}
|
nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation
| null | null | true | false |
Isensee, Fabian and Jaeger, Paul F and Kohl, Simon AA and Petersen, Jens and Maier-Hein, Klaus H
| 2,021 | null | null | null |
Nature methods
|
nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation
|
nnU-Net: a self-configuring method for deep learning-based ... - Nature
|
https://www.nature.com/articles/s41592-020-01008-z
|
# nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation We developed nnU-Net, a deep learning-based segmentation method that automatically configures itself, including preprocessing, network architecture, training and post-processing for any new task. ### Variability and reproducibility in deep learning for medical image segmentation U-net: convolutional networks for biomedical image segmentation. V-net: fully convolutional neural networks for volumetric medical image segmentation. DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Trans. F.I. and P.F.J. conceptualized the method and planned the experiments with the help of S.A.A.K., J.P. and K.H.M.-H. P.F.J., S.A.A.K. and K.H.M.-H. P.F.J., F.I. and K.H.M.-H. wrote the paper with contributions from J.P. and S.A.A.K. K.H.M.-H. nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation.
|
Efficient 3D Brain Tumor Segmentation with Axial-Coronal-Sagittal
Embedding
|
2506.00434v1
|
wang_transbts_miccai_2021
|
\cite{wang_transbts_miccai_2021}
|
TransBTS: Multimodal Brain Tumor Segmentation Using Transformer
|
http://arxiv.org/abs/2103.04430v2
|
Transformer, which can benefit from global (long-range) information modeling
using self-attention mechanisms, has been successful in natural language
processing and 2D image classification recently. However, both local and global
features are crucial for dense prediction tasks, especially for 3D medical
image segmentation. In this paper, we for the first time exploit Transformer in
3D CNN for MRI Brain Tumor Segmentation and propose a novel network named
TransBTS based on the encoder-decoder structure. To capture the local 3D
context information, the encoder first utilizes 3D CNN to extract the
volumetric spatial feature maps. Meanwhile, the feature maps are reformed
elaborately for tokens that are fed into Transformer for global feature
modeling. The decoder leverages the features embedded by Transformer and
performs progressive upsampling to predict the detailed segmentation map.
Extensive experimental results on both BraTS 2019 and 2020 datasets show that
TransBTS achieves comparable or higher results than previous state-of-the-art
3D methods for brain tumor segmentation on 3D MRI scans. The source code is
available at https://github.com/Wenxuan-1119/TransBTS
| true | true |
Wang, Wenxuan and Chen, Chen and Ding, Meng and Yu, Hong and Zha, Sen and Li, Jiangyun
| 2,021 | null | null | null | null |
TransBTS: Multimodal Brain Tumor Segmentation Using Transformer
|
TransBTS: Multimodal Brain Tumor Segmentation Using Transformer
|
http://arxiv.org/pdf/2103.04430v2
|
Transformer, which can benefit from global (long-range) information modeling
using self-attention mechanisms, has been successful in natural language
processing and 2D image classification recently. However, both local and global
features are crucial for dense prediction tasks, especially for 3D medical
image segmentation. In this paper, we for the first time exploit Transformer in
3D CNN for MRI Brain Tumor Segmentation and propose a novel network named
TransBTS based on the encoder-decoder structure. To capture the local 3D
context information, the encoder first utilizes 3D CNN to extract the
volumetric spatial feature maps. Meanwhile, the feature maps are reformed
elaborately for tokens that are fed into Transformer for global feature
modeling. The decoder leverages the features embedded by Transformer and
performs progressive upsampling to predict the detailed segmentation map.
Extensive experimental results on both BraTS 2019 and 2020 datasets show that
TransBTS achieves comparable or higher results than previous state-of-the-art
3D methods for brain tumor segmentation on 3D MRI scans. The source code is
available at https://github.com/Wenxuan-1119/TransBTS
|
Efficient 3D Brain Tumor Segmentation with Axial-Coronal-Sagittal
Embedding
|
2506.00434v1
|
swinunetr
|
\cite{swinunetr}
|
Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors
in MRI Images
|
http://arxiv.org/abs/2201.01266v1
|
Semantic segmentation of brain tumors is a fundamental medical image analysis
task involving multiple MRI imaging modalities that can assist clinicians in
diagnosing the patient and successively studying the progression of the
malignant entity. In recent years, Fully Convolutional Neural Networks (FCNNs)
approaches have become the de facto standard for 3D medical image segmentation.
The popular "U-shaped" network architecture has achieved state-of-the-art
performance benchmarks on different 2D and 3D semantic segmentation tasks and
across various imaging modalities. However, due to the limited kernel size of
convolution layers in FCNNs, their performance of modeling long-range
information is sub-optimal, and this can lead to deficiencies in the
segmentation of tumors with variable sizes. On the other hand, transformer
models have demonstrated excellent capabilities in capturing such long-range
information in multiple domains, including natural language processing and
computer vision. Inspired by the success of vision transformers and their
variants, we propose a novel segmentation model termed Swin UNEt TRansformers
(Swin UNETR). Specifically, the task of 3D brain tumor semantic segmentation is
reformulated as a sequence to sequence prediction problem wherein multi-modal
input data is projected into a 1D sequence of embedding and used as an input to
a hierarchical Swin transformer as the encoder. The swin transformer encoder
extracts features at five different resolutions by utilizing shifted windows
for computing self-attention and is connected to an FCNN-based decoder at each
resolution via skip connections. We have participated in BraTS 2021
segmentation challenge, and our proposed model ranks among the top-performing
approaches in the validation phase. Code: https://monai.io/research/swin-unetr
| true | true |
Hatamizadeh, Ali and Nath, Vishwesh and Tang, Yucheng and Yang, Dong and Roth, Holger R and Xu, Daguang
| 2,021 | null | null | null | null |
Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors
in MRI Images
|
Swin Transformers for Semantic Segmentation of Brain Tumors in ...
|
https://arxiv.org/abs/2201.01266
|
We propose a novel segmentation model termed Swin UNEt TRansformers (Swin UNETR). Specifically, the task of 3D brain tumor semantic segmentation is
|
Efficient 3D Brain Tumor Segmentation with Axial-Coronal-Sagittal
Embedding
|
2506.00434v1
|
chen_med3d_arxiv_2019
|
\cite{chen_med3d_arxiv_2019}
|
Med3D: Transfer Learning for 3D Medical Image Analysis
|
http://arxiv.org/abs/1904.00625v4
|
The performance on deep learning is significantly affected by volume of
training data. Models pre-trained from massive dataset such as ImageNet become
a powerful weapon for speeding up training convergence and improving accuracy.
Similarly, models based on large dataset are important for the development of
deep learning in 3D medical images. However, it is extremely challenging to
build a sufficiently large dataset due to difficulty of data acquisition and
annotation in 3D medical imaging. We aggregate the dataset from several medical
challenges to build 3DSeg-8 dataset with diverse modalities, target organs, and
pathologies. To extract general medical three-dimension (3D) features, we
design a heterogeneous 3D network called Med3D to co-train multi-domain 3DSeg-8
so as to make a series of pre-trained models. We transfer Med3D pre-trained
models to lung segmentation in LIDC dataset, pulmonary nodule classification in
LIDC dataset and liver segmentation on LiTS challenge. Experiments show that
the Med3D can accelerate the training convergence speed of target 3D medical
tasks 2 times compared with model pre-trained on Kinetics dataset, and 10 times
compared with training from scratch as well as improve accuracy ranging from 3%
to 20%. Transferring our Med3D model on state-the-of-art DenseASPP segmentation
network, in case of single model, we achieve 94.6\% Dice coefficient which
approaches the result of top-ranged algorithms on the LiTS challenge.
| true | true |
Chen, Sihong and Ma, Kai and Zheng, Yefeng
| 2,019 | null | null | null |
arXiv preprint arXiv:1904.00625
|
Med3D: Transfer Learning for 3D Medical Image Analysis
|
Med3D: Transfer Learning for 3D Medical Image Analysis
|
http://arxiv.org/pdf/1904.00625v4
|
The performance on deep learning is significantly affected by volume of
training data. Models pre-trained from massive dataset such as ImageNet become
a powerful weapon for speeding up training convergence and improving accuracy.
Similarly, models based on large dataset are important for the development of
deep learning in 3D medical images. However, it is extremely challenging to
build a sufficiently large dataset due to difficulty of data acquisition and
annotation in 3D medical imaging. We aggregate the dataset from several medical
challenges to build 3DSeg-8 dataset with diverse modalities, target organs, and
pathologies. To extract general medical three-dimension (3D) features, we
design a heterogeneous 3D network called Med3D to co-train multi-domain 3DSeg-8
so as to make a series of pre-trained models. We transfer Med3D pre-trained
models to lung segmentation in LIDC dataset, pulmonary nodule classification in
LIDC dataset and liver segmentation on LiTS challenge. Experiments show that
the Med3D can accelerate the training convergence speed of target 3D medical
tasks 2 times compared with model pre-trained on Kinetics dataset, and 10 times
compared with training from scratch as well as improve accuracy ranging from 3%
to 20%. Transferring our Med3D model on state-the-of-art DenseASPP segmentation
network, in case of single model, we achieve 94.6\% Dice coefficient which
approaches the result of top-ranged algorithms on the LiTS challenge.
|
Efficient 3D Brain Tumor Segmentation with Axial-Coronal-Sagittal
Embedding
|
2506.00434v1
|
zhu_modelgenesis_mia_2021
|
\cite{zhu_modelgenesis_mia_2021}
|
Models Genesis
|
http://arxiv.org/abs/2004.07882v4
|
Transfer learning from natural images to medical images has been established
as one of the most practical paradigms in deep learning for medical image
analysis. To fit this paradigm, however, 3D imaging tasks in the most prominent
imaging modalities (e.g., CT and MRI) have to be reformulated and solved in 2D,
losing rich 3D anatomical information, thereby inevitably compromising its
performance. To overcome this limitation, we have built a set of models, called
Generic Autodidactic Models, nicknamed Models Genesis, because they are created
ex nihilo (with no manual labeling), self-taught (learnt by self-supervision),
and generic (served as source models for generating application-specific target
models). Our extensive experiments demonstrate that our Models Genesis
significantly outperform learning from scratch and existing pre-trained 3D
models in all five target 3D applications covering both segmentation and
classification. More importantly, learning a model from scratch simply in 3D
may not necessarily yield performance better than transfer learning from
ImageNet in 2D, but our Models Genesis consistently top any 2D/2.5D approaches
including fine-tuning the models pre-trained from ImageNet as well as
fine-tuning the 2D versions of our Models Genesis, confirming the importance of
3D anatomical information and significance of Models Genesis for 3D medical
imaging. This performance is attributed to our unified self-supervised learning
framework, built on a simple yet powerful observation: the sophisticated and
recurrent anatomy in medical images can serve as strong yet free supervision
signals for deep models to learn common anatomical representation automatically
via self-supervision. As open science, all codes and pre-trained Models Genesis
are available at https://github.com/MrGiovanni/ModelsGenesis.
| true | true |
Zhou, Zongwei and Sodha, Vatsal and Pang, Jiaxuan and Gotway, Michael B and Liang, Jianming
| 2,021 | null | null | null |
Medical image analysis
|
Models Genesis
|
Models Genesis
|
http://arxiv.org/pdf/2004.07882v4
|
Transfer learning from natural images to medical images has been established
as one of the most practical paradigms in deep learning for medical image
analysis. To fit this paradigm, however, 3D imaging tasks in the most prominent
imaging modalities (e.g., CT and MRI) have to be reformulated and solved in 2D,
losing rich 3D anatomical information, thereby inevitably compromising its
performance. To overcome this limitation, we have built a set of models, called
Generic Autodidactic Models, nicknamed Models Genesis, because they are created
ex nihilo (with no manual labeling), self-taught (learnt by self-supervision),
and generic (served as source models for generating application-specific target
models). Our extensive experiments demonstrate that our Models Genesis
significantly outperform learning from scratch and existing pre-trained 3D
models in all five target 3D applications covering both segmentation and
classification. More importantly, learning a model from scratch simply in 3D
may not necessarily yield performance better than transfer learning from
ImageNet in 2D, but our Models Genesis consistently top any 2D/2.5D approaches
including fine-tuning the models pre-trained from ImageNet as well as
fine-tuning the 2D versions of our Models Genesis, confirming the importance of
3D anatomical information and significance of Models Genesis for 3D medical
imaging. This performance is attributed to our unified self-supervised learning
framework, built on a simple yet powerful observation: the sophisticated and
recurrent anatomy in medical images can serve as strong yet free supervision
signals for deep models to learn common anatomical representation automatically
via self-supervision. As open science, all codes and pre-trained Models Genesis
are available at https://github.com/MrGiovanni/ModelsGenesis.
|
Test-time Vocabulary Adaptation for Language-driven Object Detection
|
2506.00333v1
|
zhu2023survey
|
\cite{zhu2023survey}
|
A Survey on Open-Vocabulary Detection and Segmentation: Past, Present,
and Future
|
http://arxiv.org/abs/2307.09220v2
|
As the most fundamental scene understanding tasks, object detection and
segmentation have made tremendous progress in deep learning era. Due to the
expensive manual labeling cost, the annotated categories in existing datasets
are often small-scale and pre-defined, i.e., state-of-the-art fully-supervised
detectors and segmentors fail to generalize beyond the closed vocabulary. To
resolve this limitation, in the last few years, the community has witnessed an
increasing attention toward Open-Vocabulary Detection (OVD) and Segmentation
(OVS). By ``open-vocabulary'', we mean that the models can classify objects
beyond pre-defined categories. In this survey, we provide a comprehensive
review on recent developments of OVD and OVS. A taxonomy is first developed to
organize different tasks and methodologies. We find that the permission and
usage of weak supervision signals can well discriminate different
methodologies, including: visual-semantic space mapping, novel visual feature
synthesis, region-aware training, pseudo-labeling, knowledge distillation, and
transfer learning. The proposed taxonomy is universal across different tasks,
covering object detection, semantic/instance/panoptic segmentation, 3D and
video understanding. The main design principles, key challenges, development
routes, methodology strengths, and weaknesses are thoroughly analyzed. In
addition, we benchmark each task along with the vital components of each method
in appendix and updated online at
https://github.com/seanzhuh/awesome-open-vocabulary-detection-and-segmentation.
Finally, several promising directions are provided and discussed to stimulate
future research.
| true | true |
Zhu, Chaoyang and Chen, Long
| 2,023 | null | null | null | null |
A Survey on Open-Vocabulary Detection and Segmentation: Past, Present,
and Future
|
Awesome OVD-OVS - A Survey on Open-Vocabulary ...
|
https://github.com/seanzhuh/Awesome-Open-Vocabulary-Detection-and-Segmentation
|
Awesome OVD-OVS - A Survey on Open-Vocabulary Detection and Segmentation: Past, Present, and Future
|
Test-time Vocabulary Adaptation for Language-driven Object Detection
|
2506.00333v1
|
radford2021learning
|
\cite{radford2021learning}
|
Learning Transferable Visual Models From Natural Language Supervision
|
http://arxiv.org/abs/2103.00020v1
|
State-of-the-art computer vision systems are trained to predict a fixed set
of predetermined object categories. This restricted form of supervision limits
their generality and usability since additional labeled data is needed to
specify any other visual concept. Learning directly from raw text about images
is a promising alternative which leverages a much broader source of
supervision. We demonstrate that the simple pre-training task of predicting
which caption goes with which image is an efficient and scalable way to learn
SOTA image representations from scratch on a dataset of 400 million (image,
text) pairs collected from the internet. After pre-training, natural language
is used to reference learned visual concepts (or describe new ones) enabling
zero-shot transfer of the model to downstream tasks. We study the performance
of this approach by benchmarking on over 30 different existing computer vision
datasets, spanning tasks such as OCR, action recognition in videos,
geo-localization, and many types of fine-grained object classification. The
model transfers non-trivially to most tasks and is often competitive with a
fully supervised baseline without the need for any dataset specific training.
For instance, we match the accuracy of the original ResNet-50 on ImageNet
zero-shot without needing to use any of the 1.28 million training examples it
was trained on. We release our code and pre-trained model weights at
https://github.com/OpenAI/CLIP.
| true | true |
Radford, Alec and Kim, Jong Wook and Hallacy, Chris and Ramesh, Aditya and Goh, Gabriel and Agarwal, Sandhini and Sastry, Girish and Askell, Amanda and Mishkin, Pamela and Clark, Jack and Krueger, Gretchen and Sutskever, Ilya
| 2,021 | null | null | null | null |
Learning Transferable Visual Models From Natural Language Supervision
|
Learning Transferable Visual Models From Natural Language Supervision
|
http://arxiv.org/pdf/2103.00020v1
|
State-of-the-art computer vision systems are trained to predict a fixed set
of predetermined object categories. This restricted form of supervision limits
their generality and usability since additional labeled data is needed to
specify any other visual concept. Learning directly from raw text about images
is a promising alternative which leverages a much broader source of
supervision. We demonstrate that the simple pre-training task of predicting
which caption goes with which image is an efficient and scalable way to learn
SOTA image representations from scratch on a dataset of 400 million (image,
text) pairs collected from the internet. After pre-training, natural language
is used to reference learned visual concepts (or describe new ones) enabling
zero-shot transfer of the model to downstream tasks. We study the performance
of this approach by benchmarking on over 30 different existing computer vision
datasets, spanning tasks such as OCR, action recognition in videos,
geo-localization, and many types of fine-grained object classification. The
model transfers non-trivially to most tasks and is often competitive with a
fully supervised baseline without the need for any dataset specific training.
For instance, we match the accuracy of the original ResNet-50 on ImageNet
zero-shot without needing to use any of the 1.28 million training examples it
was trained on. We release our code and pre-trained model weights at
https://github.com/OpenAI/CLIP.
|
Test-time Vocabulary Adaptation for Language-driven Object Detection
|
2506.00333v1
|
lin2014microsoft
|
\cite{lin2014microsoft}
|
Microsoft COCO: Common Objects in Context
|
http://arxiv.org/abs/1405.0312v3
|
We present a new dataset with the goal of advancing the state-of-the-art in
object recognition by placing the question of object recognition in the context
of the broader question of scene understanding. This is achieved by gathering
images of complex everyday scenes containing common objects in their natural
context. Objects are labeled using per-instance segmentations to aid in precise
object localization. Our dataset contains photos of 91 objects types that would
be easily recognizable by a 4 year old. With a total of 2.5 million labeled
instances in 328k images, the creation of our dataset drew upon extensive crowd
worker involvement via novel user interfaces for category detection, instance
spotting and instance segmentation. We present a detailed statistical analysis
of the dataset in comparison to PASCAL, ImageNet, and SUN. Finally, we provide
baseline performance analysis for bounding box and segmentation detection
results using a Deformable Parts Model.
| true | true |
Lin, Tsung-Yi and Maire, Michael and Belongie, Serge and Hays, James and Perona, Pietro and Ramanan, Deva and Doll{\'a}r, Piotr and Zitnick, C. Lawrence
| 2,014 | null | null | null | null |
Microsoft COCO: Common Objects in Context
|
Microsoft COCO: Common Objects in Context
|
http://arxiv.org/pdf/1405.0312v3
|
We present a new dataset with the goal of advancing the state-of-the-art in
object recognition by placing the question of object recognition in the context
of the broader question of scene understanding. This is achieved by gathering
images of complex everyday scenes containing common objects in their natural
context. Objects are labeled using per-instance segmentations to aid in precise
object localization. Our dataset contains photos of 91 objects types that would
be easily recognizable by a 4 year old. With a total of 2.5 million labeled
instances in 328k images, the creation of our dataset drew upon extensive crowd
worker involvement via novel user interfaces for category detection, instance
spotting and instance segmentation. We present a detailed statistical analysis
of the dataset in comparison to PASCAL, ImageNet, and SUN. Finally, we provide
baseline performance analysis for bounding box and segmentation detection
results using a Deformable Parts Model.
|
Test-time Vocabulary Adaptation for Language-driven Object Detection
|
2506.00333v1
|
gupta2019lvis
|
\cite{gupta2019lvis}
|
LVIS: A Dataset for Large Vocabulary Instance Segmentation
|
http://arxiv.org/abs/1908.03195v2
|
Progress on object detection is enabled by datasets that focus the research
community's attention on open challenges. This process led us from simple
images to complex scenes and from bounding boxes to segmentation masks. In this
work, we introduce LVIS (pronounced `el-vis'): a new dataset for Large
Vocabulary Instance Segmentation. We plan to collect ~2 million high-quality
instance segmentation masks for over 1000 entry-level object categories in 164k
images. Due to the Zipfian distribution of categories in natural images, LVIS
naturally has a long tail of categories with few training samples. Given that
state-of-the-art deep learning methods for object detection perform poorly in
the low-sample regime, we believe that our dataset poses an important and
exciting new scientific challenge. LVIS is available at
http://www.lvisdataset.org.
| true | true |
Gupta, Agrim and Dollar, Piotr and Girshick, Ross
| 2,019 | null | null | null | null |
LVIS: A Dataset for Large Vocabulary Instance Segmentation
|
LVIS: A Dataset for Large Vocabulary Instance Segmentation
|
http://arxiv.org/pdf/1908.03195v2
|
Progress on object detection is enabled by datasets that focus the research
community's attention on open challenges. This process led us from simple
images to complex scenes and from bounding boxes to segmentation masks. In this
work, we introduce LVIS (pronounced `el-vis'): a new dataset for Large
Vocabulary Instance Segmentation. We plan to collect ~2 million high-quality
instance segmentation masks for over 1000 entry-level object categories in 164k
images. Due to the Zipfian distribution of categories in natural images, LVIS
naturally has a long tail of categories with few training samples. Given that
state-of-the-art deep learning methods for object detection perform poorly in
the low-sample regime, we believe that our dataset poses an important and
exciting new scientific challenge. LVIS is available at
http://www.lvisdataset.org.
|
Test-time Vocabulary Adaptation for Language-driven Object Detection
|
2506.00333v1
|
deng2009imagenet
|
\cite{deng2009imagenet}
|
{ImageNet: a Large-Scale Hierarchical Image Database}
| null | null | true | false |
Deng, Jia and Dong, Wei and Socher, Richard and Li, Li-Jia and Li, Kai and Fei-Fei, Li
| 2,009 | null | null | null | null |
{ImageNet: a Large-Scale Hierarchical Image Database}
|
(PDF) ImageNet: a Large-Scale Hierarchical Image Database
|
https://www.researchgate.net/publication/221361415_ImageNet_a_Large-Scale_Hierarchical_Image_Database
|
This paper offers a detailed analysis of ImageNet in its current state: 12 subtrees with 5247 synsets and 3.2 million images in total.
|
Test-time Vocabulary Adaptation for Language-driven Object Detection
|
2506.00333v1
|
zhou2022detecting
|
\cite{zhou2022detecting}
|
Detecting Twenty-thousand Classes using Image-level Supervision
|
http://arxiv.org/abs/2201.02605v3
|
Current object detectors are limited in vocabulary size due to the small
scale of detection datasets. Image classifiers, on the other hand, reason about
much larger vocabularies, as their datasets are larger and easier to collect.
We propose Detic, which simply trains the classifiers of a detector on image
classification data and thus expands the vocabulary of detectors to tens of
thousands of concepts. Unlike prior work, Detic does not need complex
assignment schemes to assign image labels to boxes based on model predictions,
making it much easier to implement and compatible with a range of detection
architectures and backbones. Our results show that Detic yields excellent
detectors even for classes without box annotations. It outperforms prior work
on both open-vocabulary and long-tail detection benchmarks. Detic provides a
gain of 2.4 mAP for all classes and 8.3 mAP for novel classes on the
open-vocabulary LVIS benchmark. On the standard LVIS benchmark, Detic obtains
41.7 mAP when evaluated on all classes, or only rare classes, hence closing the
gap in performance for object categories with few samples. For the first time,
we train a detector with all the twenty-one-thousand classes of the ImageNet
dataset and show that it generalizes to new datasets without finetuning. Code
is available at \url{https://github.com/facebookresearch/Detic}.
| true | true |
Zhou, Xingyi and Girdhar, Rohit and Joulin, Armand and Kr{\"a}henb{\"u}hl, Philipp and Misra, Ishan
| 2,022 | null | null | null | null |
Detecting Twenty-thousand Classes using Image-level Supervision
|
[PDF] Detecting Twenty-thousand Classes using Image-level Supervision
|
https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136690344.pdf
|
We propose Detic, which simply trains the classifiers of a detector on image classification data and thus expands the vocabulary of detectors to tens of
|
Test-time Vocabulary Adaptation for Language-driven Object Detection
|
2506.00333v1
|
zhong2022regionclip
|
\cite{zhong2022regionclip}
|
RegionCLIP: Region-based Language-Image Pretraining
|
http://arxiv.org/abs/2112.09106v1
|
Contrastive language-image pretraining (CLIP) using image-text pairs has
achieved impressive results on image classification in both zero-shot and
transfer learning settings. However, we show that directly applying such models
to recognize image regions for object detection leads to poor performance due
to a domain shift: CLIP was trained to match an image as a whole to a text
description, without capturing the fine-grained alignment between image regions
and text spans. To mitigate this issue, we propose a new method called
RegionCLIP that significantly extends CLIP to learn region-level visual
representations, thus enabling fine-grained alignment between image regions and
textual concepts. Our method leverages a CLIP model to match image regions with
template captions and then pretrains our model to align these region-text pairs
in the feature space. When transferring our pretrained model to the
open-vocabulary object detection tasks, our method significantly outperforms
the state of the art by 3.8 AP50 and 2.2 AP for novel categories on COCO and
LVIS datasets, respectively. Moreoever, the learned region representations
support zero-shot inference for object detection, showing promising results on
both COCO and LVIS datasets. Our code is available at
https://github.com/microsoft/RegionCLIP.
| true | true |
Zhong, Yiwu and Yang, Jianwei and Zhang, Pengchuan and Li, Chunyuan and Codella, Noel and Li, Liunian Harold and Zhou, Luowei and Dai, Xiyang and Yuan, Lu and Li, Yin and Gao, Jianfeng
| 2,022 | null | null | null | null |
RegionCLIP: Region-based Language-Image Pretraining
|
RegionCLIP: Region-based Language-Image Pretraining - arXiv
|
https://arxiv.org/abs/2112.09106
|
We propose a new method called RegionCLIP that significantly extends CLIP to learn region-level visual representations, thus enabling fine-grained alignment.
|
Test-time Vocabulary Adaptation for Language-driven Object Detection
|
2506.00333v1
|
ma2024codet
|
\cite{ma2024codet}
|
CoDet: Co-Occurrence Guided Region-Word Alignment for Open-Vocabulary
Object Detection
|
http://arxiv.org/abs/2310.16667v1
|
Deriving reliable region-word alignment from image-text pairs is critical to
learn object-level vision-language representations for open-vocabulary object
detection. Existing methods typically rely on pre-trained or self-trained
vision-language models for alignment, which are prone to limitations in
localization accuracy or generalization capabilities. In this paper, we propose
CoDet, a novel approach that overcomes the reliance on pre-aligned
vision-language space by reformulating region-word alignment as a co-occurring
object discovery problem. Intuitively, by grouping images that mention a shared
concept in their captions, objects corresponding to the shared concept shall
exhibit high co-occurrence among the group. CoDet then leverages visual
similarities to discover the co-occurring objects and align them with the
shared concept. Extensive experiments demonstrate that CoDet has superior
performances and compelling scalability in open-vocabulary detection, e.g., by
scaling up the visual backbone, CoDet achieves 37.0 $\text{AP}^m_{novel}$ and
44.7 $\text{AP}^m_{all}$ on OV-LVIS, surpassing the previous SoTA by 4.2
$\text{AP}^m_{novel}$ and 9.8 $\text{AP}^m_{all}$. Code is available at
https://github.com/CVMI-Lab/CoDet.
| true | true |
Ma, Chuofan and Jiang, Yi and Wen, Xin and Yuan, Zehuan and Qi, Xiaojuan
| 2,023 | null | null | null | null |
CoDet: Co-Occurrence Guided Region-Word Alignment for Open-Vocabulary
Object Detection
|
(NeurIPS2023) CoDet: Co-Occurrence Guided Region ...
|
https://github.com/CVMI-Lab/CoDet
|
Train an open-vocabulary detector with web-scale image-text pairs; Align regions and words by co-occurrence instead of region-text similarity
|
Test-time Vocabulary Adaptation for Language-driven Object Detection
|
2506.00333v1
|
liu2024shine
|
\cite{liu2024shine}
|
SHiNe: Semantic Hierarchy Nexus for Open-vocabulary Object Detection
|
http://arxiv.org/abs/2405.10053v1
|
Open-vocabulary object detection (OvOD) has transformed detection into a
language-guided task, empowering users to freely define their class
vocabularies of interest during inference. However, our initial investigation
indicates that existing OvOD detectors exhibit significant variability when
dealing with vocabularies across various semantic granularities, posing a
concern for real-world deployment. To this end, we introduce Semantic Hierarchy
Nexus (SHiNe), a novel classifier that uses semantic knowledge from class
hierarchies. It runs offline in three steps: i) it retrieves relevant
super-/sub-categories from a hierarchy for each target class; ii) it integrates
these categories into hierarchy-aware sentences; iii) it fuses these sentence
embeddings to generate the nexus classifier vector. Our evaluation on various
detection benchmarks demonstrates that SHiNe enhances robustness across diverse
vocabulary granularities, achieving up to +31.9% mAP50 with ground truth
hierarchies, while retaining improvements using hierarchies generated by large
language models. Moreover, when applied to open-vocabulary classification on
ImageNet-1k, SHiNe improves the CLIP zero-shot baseline by +2.8% accuracy.
SHiNe is training-free and can be seamlessly integrated with any off-the-shelf
OvOD detector, without incurring additional computational overhead during
inference. The code is open source.
| true | true |
Liu, Mingxuan and Hayes, Tyler L. and Ricci, Elisa and Csurka, Gabriela and Volpi, Riccardo
| 2,024 | null | null | null | null |
SHiNe: Semantic Hierarchy Nexus for Open-vocabulary Object Detection
|
[PDF] Semantic Hierarchy Nexus for Open-vocabulary Object Detection
|
https://openaccess.thecvf.com/content/CVPR2024/papers/Liu_SHiNe_Semantic_Hierarchy_Nexus_for_Open-vocabulary_Object_Detection_CVPR_2024_paper.pdf
|
SHiNe is training-free and can be seamlessly integrated with any off-the-shelf OvOD detector, without incurring additional computational overhead dur- ing
|
Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised
Learning with Outliers
|
2505.24443v1
|
ssl_2
|
\cite{ssl_2}
|
Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks
| null | null | true | false |
Lee, Dong-Hyun
| 2,013 | null | null | null | null |
Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks
|
Pseudo-Label : The Simple and Efficient Semi-Supervised ...
|
https://www.researchgate.net/publication/280581078_Pseudo-Label_The_Simple_and_Efficient_Semi-Supervised_Learning_Method_for_Deep_Neural_Networks
|
We propose the simple and efficient method of semi-supervised learning for deep neural networks. Basically, the proposed network is trained in a supervised
|
Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised
Learning with Outliers
|
2505.24443v1
|
ssl_9
|
\cite{ssl_9}
|
Semi-supervised Learning by Entropy Minimization
| null | null | true | false |
Yves Grandvalet and
Yoshua Bengio
| 2,004 | null | null | null | null |
Semi-supervised Learning by Entropy Minimization
|
Semi-supervised Learning by Entropy Minimization - NIPS
|
https://papers.nips.cc/paper/2740-semi-supervised-learning-by-entropy-minimization
|
We consider the semi-supervised learning problem, where a decision rule is to be learned from labeled and unlabeled data. In this framework, we motivate minimum entropy regularization, which enables to incorporate unlabeled data in the standard supervised learning. In the terminology used here, semi-supervised learning refers to learning a decision rule on X from labeled and unlabeled data. In the probabilistic framework, semi-supervised learning can be modeled as a missing data problem, which can be addressed by generative models such as mixture models thanks to the EM algorithm and extensions thereof .Generative models apply to the joint den- sity of patterns and class (X, Y ). Authors are asked to consider this carefully and discuss it with their co-authors prior to requesting a name change in the electronic proceedings.
|
Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised
Learning with Outliers
|
2505.24443v1
|
ssl_10
|
\cite{ssl_10}
|
Curriculum Labeling: Revisiting Pseudo-Labeling for Semi-Supervised
Learning
|
http://arxiv.org/abs/2001.06001v2
|
In this paper we revisit the idea of pseudo-labeling in the context of
semi-supervised learning where a learning algorithm has access to a small set
of labeled samples and a large set of unlabeled samples. Pseudo-labeling works
by applying pseudo-labels to samples in the unlabeled set by using a model
trained on the combination of the labeled samples and any previously
pseudo-labeled samples, and iteratively repeating this process in a
self-training cycle. Current methods seem to have abandoned this approach in
favor of consistency regularization methods that train models under a
combination of different styles of self-supervised losses on the unlabeled
samples and standard supervised losses on the labeled samples. We empirically
demonstrate that pseudo-labeling can in fact be competitive with the
state-of-the-art, while being more resilient to out-of-distribution samples in
the unlabeled set. We identify two key factors that allow pseudo-labeling to
achieve such remarkable results (1) applying curriculum learning principles and
(2) avoiding concept drift by restarting model parameters before each
self-training cycle. We obtain 94.91% accuracy on CIFAR-10 using only 4,000
labeled samples, and 68.87% top-1 accuracy on Imagenet-ILSVRC using only 10% of
the labeled samples. The code is available at
https://github.com/uvavision/Curriculum-Labeling
| true | true |
Paola Cascante{-}Bonilla and
Fuwen Tan and
Yanjun Qi and
Vicente Ordonez
| 2,021 | null | null | null | null |
Curriculum Labeling: Revisiting Pseudo-Labeling for Semi-Supervised
Learning
|
Revisiting Pseudo-Labeling for Semi-Supervised Learning
|
https://arxiv.org/abs/2001.06001
|
by P Cascante-Bonilla · 2020 · Cited by 409 — In this paper we revisit the idea of pseudo-labeling in the context of semi-supervised learning where a learning algorithm has access to a small set of labeled
|
Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised
Learning with Outliers
|
2505.24443v1
|
ssl_11
|
\cite{ssl_11}
|
Mean teachers are better role models: Weight-averaged consistency
targets improve semi-supervised deep learning results
|
http://arxiv.org/abs/1703.01780v6
|
The recently proposed Temporal Ensembling has achieved state-of-the-art
results in several semi-supervised learning benchmarks. It maintains an
exponential moving average of label predictions on each training example, and
penalizes predictions that are inconsistent with this target. However, because
the targets change only once per epoch, Temporal Ensembling becomes unwieldy
when learning large datasets. To overcome this problem, we propose Mean
Teacher, a method that averages model weights instead of label predictions. As
an additional benefit, Mean Teacher improves test accuracy and enables training
with fewer labels than Temporal Ensembling. Without changing the network
architecture, Mean Teacher achieves an error rate of 4.35% on SVHN with 250
labels, outperforming Temporal Ensembling trained with 1000 labels. We also
show that a good network architecture is crucial to performance. Combining Mean
Teacher and Residual Networks, we improve the state of the art on CIFAR-10 with
4000 labels from 10.55% to 6.28%, and on ImageNet 2012 with 10% of the labels
from 35.24% to 9.11%.
| true | true |
Antti Tarvainen and
Harri Valpola
| 2,017 | null | null | null | null |
Mean teachers are better role models: Weight-averaged consistency
targets improve semi-supervised deep learning results
|
[PDF] Weight-averaged consistency targets improve semi-supervised ...
|
https://arxiv.org/pdf/1703.01780
|
Combining Mean Teacher and Residual Networks, we improve the state of the art on CIFAR-10 with 4000 labels from 10.55% to 6.28%, and on.
|
Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised
Learning with Outliers
|
2505.24443v1
|
ssl_12
|
\cite{ssl_12}
|
Regularization With Stochastic Transformations and Perturbations for
Deep Semi-Supervised Learning
|
http://arxiv.org/abs/1606.04586v1
|
Effective convolutional neural networks are trained on large sets of labeled
data. However, creating large labeled datasets is a very costly and
time-consuming task. Semi-supervised learning uses unlabeled data to train a
model with higher accuracy when there is a limited set of labeled data
available. In this paper, we consider the problem of semi-supervised learning
with convolutional neural networks. Techniques such as randomized data
augmentation, dropout and random max-pooling provide better generalization and
stability for classifiers that are trained using gradient descent. Multiple
passes of an individual sample through the network might lead to different
predictions due to the non-deterministic behavior of these techniques. We
propose an unsupervised loss function that takes advantage of the stochastic
nature of these methods and minimizes the difference between the predictions of
multiple passes of a training sample through the network. We evaluate the
proposed method on several benchmark datasets.
| true | true |
Mehdi Sajjadi and
Mehran Javanmardi and
Tolga Tasdizen
| 2,016 | null | null | null | null |
Regularization With Stochastic Transformations and Perturbations for
Deep Semi-Supervised Learning
|
Regularization With Stochastic Transformations and Perturbations ...
|
https://arxiv.org/abs/1606.04586
|
Abstract page for arXiv paper 1606.04586: Regularization With Stochastic Transformations and Perturbations for Deep Semi-Supervised Learning.
|
Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised
Learning with Outliers
|
2505.24443v1
|
ssl_13
|
\cite{ssl_13}
|
Temporal Ensembling for Semi-Supervised Learning
|
http://arxiv.org/abs/1610.02242v3
|
In this paper, we present a simple and efficient method for training deep
neural networks in a semi-supervised setting where only a small portion of
training data is labeled. We introduce self-ensembling, where we form a
consensus prediction of the unknown labels using the outputs of the
network-in-training on different epochs, and most importantly, under different
regularization and input augmentation conditions. This ensemble prediction can
be expected to be a better predictor for the unknown labels than the output of
the network at the most recent training epoch, and can thus be used as a target
for training. Using our method, we set new records for two standard
semi-supervised learning benchmarks, reducing the (non-augmented)
classification error rate from 18.44% to 7.05% in SVHN with 500 labels and from
18.63% to 16.55% in CIFAR-10 with 4000 labels, and further to 5.12% and 12.16%
by enabling the standard augmentations. We additionally obtain a clear
improvement in CIFAR-100 classification accuracy by using random images from
the Tiny Images dataset as unlabeled extra inputs during training. Finally, we
demonstrate good tolerance to incorrect labels.
| true | true |
Samuli Laine and
Timo Aila
| 2,017 | null | null | null | null |
Temporal Ensembling for Semi-Supervised Learning
|
Review — Π-Model, Temporal Ensembling ... - Sik-Ho Tsang
|
https://sh-tsang.medium.com/review-%CF%80-model-temporal-ensembling-temporal-ensembling-for-semi-supervised-learning-9cb6eea6865e
|
Temporal Ensembling for Semi-Supervised Learning. Stochastic Augmentation, Network Dropout, & Momentum Encoder are Used.
|
Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised
Learning with Outliers
|
2505.24443v1
|
ssl_14
|
\cite{ssl_14}
|
Unsupervised Data Augmentation for Consistency Training
|
http://arxiv.org/abs/1904.12848v6
|
Semi-supervised learning lately has shown much promise in improving deep
learning models when labeled data is scarce. Common among recent approaches is
the use of consistency training on a large amount of unlabeled data to
constrain model predictions to be invariant to input noise. In this work, we
present a new perspective on how to effectively noise unlabeled examples and
argue that the quality of noising, specifically those produced by advanced data
augmentation methods, plays a crucial role in semi-supervised learning. By
substituting simple noising operations with advanced data augmentation methods
such as RandAugment and back-translation, our method brings substantial
improvements across six language and three vision tasks under the same
consistency training framework. On the IMDb text classification dataset, with
only 20 labeled examples, our method achieves an error rate of 4.20,
outperforming the state-of-the-art model trained on 25,000 labeled examples. On
a standard semi-supervised learning benchmark, CIFAR-10, our method outperforms
all previous approaches and achieves an error rate of 5.43 with only 250
examples. Our method also combines well with transfer learning, e.g., when
finetuning from BERT, and yields improvements in high-data regime, such as
ImageNet, whether when there is only 10% labeled data or when a full labeled
set with 1.3M extra unlabeled examples is used. Code is available at
https://github.com/google-research/uda.
| true | true |
Qizhe Xie and
Zihang Dai and
Eduard H. Hovy and
Thang Luong and
Quoc Le
| 2,020 | null | null | null | null |
Unsupervised Data Augmentation for Consistency Training
|
Unsupervised Data Augmentation for Consistency Training
|
http://arxiv.org/pdf/1904.12848v6
|
Semi-supervised learning lately has shown much promise in improving deep
learning models when labeled data is scarce. Common among recent approaches is
the use of consistency training on a large amount of unlabeled data to
constrain model predictions to be invariant to input noise. In this work, we
present a new perspective on how to effectively noise unlabeled examples and
argue that the quality of noising, specifically those produced by advanced data
augmentation methods, plays a crucial role in semi-supervised learning. By
substituting simple noising operations with advanced data augmentation methods
such as RandAugment and back-translation, our method brings substantial
improvements across six language and three vision tasks under the same
consistency training framework. On the IMDb text classification dataset, with
only 20 labeled examples, our method achieves an error rate of 4.20,
outperforming the state-of-the-art model trained on 25,000 labeled examples. On
a standard semi-supervised learning benchmark, CIFAR-10, our method outperforms
all previous approaches and achieves an error rate of 5.43 with only 250
examples. Our method also combines well with transfer learning, e.g., when
finetuning from BERT, and yields improvements in high-data regime, such as
ImageNet, whether when there is only 10% labeled data or when a full labeled
set with 1.3M extra unlabeled examples is used. Code is available at
https://github.com/google-research/uda.
|
Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised
Learning with Outliers
|
2505.24443v1
|
tnnls_2
|
\cite{tnnls_2}
|
MutexMatch: Semi-Supervised Learning with Mutex-Based Consistency
Regularization
|
http://arxiv.org/abs/2203.14316v2
|
The core issue in semi-supervised learning (SSL) lies in how to effectively
leverage unlabeled data, whereas most existing methods tend to put a great
emphasis on the utilization of high-confidence samples yet seldom fully explore
the usage of low-confidence samples. In this paper, we aim to utilize
low-confidence samples in a novel way with our proposed mutex-based consistency
regularization, namely MutexMatch. Specifically, the high-confidence samples
are required to exactly predict "what it is" by conventional True-Positive
Classifier, while the low-confidence samples are employed to achieve a simpler
goal -- to predict with ease "what it is not" by True-Negative Classifier. In
this sense, we not only mitigate the pseudo-labeling errors but also make full
use of the low-confidence unlabeled data by consistency of dissimilarity
degree. MutexMatch achieves superior performance on multiple benchmark
datasets, i.e., CIFAR-10, CIFAR-100, SVHN, STL-10, mini-ImageNet and
Tiny-ImageNet. More importantly, our method further shows superiority when the
amount of labeled data is scarce, e.g., 92.23% accuracy with only 20 labeled
data on CIFAR-10. Our code and model weights have been released at
https://github.com/NJUyued/MutexMatch4SSL.
| true | true |
Yue Duan and
Zhen Zhao and
Lei Qi and
Lei Wang and
Luping Zhou and
Yinghuan Shi and
Yang Gao
| 2,024 | null | null | null |
{IEEE} Trans. on Neural Networks and Learning Systems
|
MutexMatch: Semi-Supervised Learning with Mutex-Based Consistency
Regularization
|
MutexMatch: Semi-Supervised Learning with Mutex-Based ... - arXiv
|
https://arxiv.org/abs/2203.14316
|
In this paper, we aim to utilize low-confidence samples in a novel way with our proposed mutex-based consistency regularization, namely MutexMatch.
|
Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised
Learning with Outliers
|
2505.24443v1
|
ssl_3
|
\cite{ssl_3}
|
MixMatch: A Holistic Approach to Semi-Supervised Learning
|
http://arxiv.org/abs/1905.02249v2
|
Semi-supervised learning has proven to be a powerful paradigm for leveraging
unlabeled data to mitigate the reliance on large labeled datasets. In this
work, we unify the current dominant approaches for semi-supervised learning to
produce a new algorithm, MixMatch, that works by guessing low-entropy labels
for data-augmented unlabeled examples and mixing labeled and unlabeled data
using MixUp. We show that MixMatch obtains state-of-the-art results by a large
margin across many datasets and labeled data amounts. For example, on CIFAR-10
with 250 labels, we reduce error rate by a factor of 4 (from 38% to 11%) and by
a factor of 2 on STL-10. We also demonstrate how MixMatch can help achieve a
dramatically better accuracy-privacy trade-off for differential privacy.
Finally, we perform an ablation study to tease apart which components of
MixMatch are most important for its success.
| true | true |
David Berthelot and
Nicholas Carlini and
Ian J. Goodfellow and
Nicolas Papernot and
Avital Oliver and
Colin Raffel
| 2,019 | null | null | null | null |
MixMatch: A Holistic Approach to Semi-Supervised Learning
|
MixMatch: a holistic approach to semi-supervised learning
|
https://dl.acm.org/doi/10.5555/3454287.3454741
|
A new algorithm, MixMatch, that guesses low-entropy labels for data-augmented un-labeled examples and mixes labeled and unlabeled data using MixUp.
|
Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised
Learning with Outliers
|
2505.24443v1
|
ssl_4
|
\cite{ssl_4}
|
FixMatch: Simplifying Semi-Supervised Learning with Consistency and
Confidence
|
http://arxiv.org/abs/2001.07685v2
|
Semi-supervised learning (SSL) provides an effective means of leveraging
unlabeled data to improve a model's performance. In this paper, we demonstrate
the power of a simple combination of two common SSL methods: consistency
regularization and pseudo-labeling. Our algorithm, FixMatch, first generates
pseudo-labels using the model's predictions on weakly-augmented unlabeled
images. For a given image, the pseudo-label is only retained if the model
produces a high-confidence prediction. The model is then trained to predict the
pseudo-label when fed a strongly-augmented version of the same image. Despite
its simplicity, we show that FixMatch achieves state-of-the-art performance
across a variety of standard semi-supervised learning benchmarks, including
94.93% accuracy on CIFAR-10 with 250 labels and 88.61% accuracy with 40 -- just
4 labels per class. Since FixMatch bears many similarities to existing SSL
methods that achieve worse performance, we carry out an extensive ablation
study to tease apart the experimental factors that are most important to
FixMatch's success. We make our code available at
https://github.com/google-research/fixmatch.
| true | true |
Kihyuk Sohn and
David Berthelot and
Nicholas Carlini and
Zizhao Zhang and
Han Zhang and
Colin Raffel and
Ekin Dogus Cubuk and
Alexey Kurakin and
Chun{-}Liang Li
| 2,020 | null | null | null | null |
FixMatch: Simplifying Semi-Supervised Learning with Consistency and
Confidence
|
FixMatch: simplifying semi-supervised learning with consistency and ...
|
https://dl.acm.org/doi/abs/10.5555/3495724.3495775
|
In this paper we propose FixMatch, an algorithm that is a significant simplification of existing SSL methods.
|
Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised
Learning with Outliers
|
2505.24443v1
|
ssl_16
|
\cite{ssl_16}
|
ReMixMatch: Semi-Supervised Learning with Distribution Alignment and
Augmentation Anchoring
|
http://arxiv.org/abs/1911.09785v2
|
We improve the recently-proposed "MixMatch" semi-supervised learning
algorithm by introducing two new techniques: distribution alignment and
augmentation anchoring. Distribution alignment encourages the marginal
distribution of predictions on unlabeled data to be close to the marginal
distribution of ground-truth labels. Augmentation anchoring feeds multiple
strongly augmented versions of an input into the model and encourages each
output to be close to the prediction for a weakly-augmented version of the same
input. To produce strong augmentations, we propose a variant of AutoAugment
which learns the augmentation policy while the model is being trained. Our new
algorithm, dubbed ReMixMatch, is significantly more data-efficient than prior
work, requiring between $5\times$ and $16\times$ less data to reach the same
accuracy. For example, on CIFAR-10 with 250 labeled examples we reach $93.73\%$
accuracy (compared to MixMatch's accuracy of $93.58\%$ with $4{,}000$ examples)
and a median accuracy of $84.92\%$ with just four labels per class. We make our
code and data open-source at https://github.com/google-research/remixmatch.
| true | true |
David Berthelot and Nicholas Carlini and Ekin D. Cubuk and Alex Kurakin and Kihyuk Sohn and Han Zhang and Colin Raffel
| 2,020 | null | null | null | null |
ReMixMatch: Semi-Supervised Learning with Distribution Alignment and
Augmentation Anchoring
|
ReMixMatch: Semi-Supervised Learning with Distribution Alignment ...
|
https://arxiv.org/abs/1911.09785
|
We improve the recently-proposed "MixMatch" semi-supervised learning algorithm by introducing two new techniques: distribution alignment and augmentation
|
Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised
Learning with Outliers
|
2505.24443v1
|
ssl_19
|
\cite{ssl_19}
|
FlexMatch: Boosting Semi-Supervised Learning with Curriculum Pseudo
Labeling
|
http://arxiv.org/abs/2110.08263v3
|
The recently proposed FixMatch achieved state-of-the-art results on most
semi-supervised learning (SSL) benchmarks. However, like other modern SSL
algorithms, FixMatch uses a pre-defined constant threshold for all classes to
select unlabeled data that contribute to the training, thus failing to consider
different learning status and learning difficulties of different classes. To
address this issue, we propose Curriculum Pseudo Labeling (CPL), a curriculum
learning approach to leverage unlabeled data according to the model's learning
status. The core of CPL is to flexibly adjust thresholds for different classes
at each time step to let pass informative unlabeled data and their pseudo
labels. CPL does not introduce additional parameters or computations (forward
or backward propagation). We apply CPL to FixMatch and call our improved
algorithm FlexMatch. FlexMatch achieves state-of-the-art performance on a
variety of SSL benchmarks, with especially strong performances when the labeled
data are extremely limited or when the task is challenging. For example,
FlexMatch achieves 13.96% and 18.96% error rate reduction over FixMatch on
CIFAR-100 and STL-10 datasets respectively, when there are only 4 labels per
class. CPL also significantly boosts the convergence speed, e.g., FlexMatch can
use only 1/5 training time of FixMatch to achieve even better performance.
Furthermore, we show that CPL can be easily adapted to other SSL algorithms and
remarkably improve their performances. We open-source our code at
https://github.com/TorchSSL/TorchSSL.
| true | true |
Zhang, Bowen and Wang, Yidong and Hou, Wenxin and Wu, Hao and Wang, Jindong and Okumura, Manabu and Shinozaki, Takahiro
| 2,021 | null | null | null | null |
FlexMatch: Boosting Semi-Supervised Learning with Curriculum Pseudo
Labeling
|
Boosting Semi-Supervised Learning with Curriculum Pseudo Labeling
|
https://arxiv.org/abs/2110.08263
|
We propose Curriculum Pseudo Labeling (CPL), a curriculum learning approach to leverage unlabeled data according to the model's learning status.
|
Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised
Learning with Outliers
|
2505.24443v1
|
ssl_20
|
\cite{ssl_20}
|
FreeMatch: Self-adaptive Thresholding for Semi-supervised Learning
|
http://arxiv.org/abs/2205.07246v3
|
Semi-supervised Learning (SSL) has witnessed great success owing to the
impressive performances brought by various methods based on pseudo labeling and
consistency regularization. However, we argue that existing methods might fail
to utilize the unlabeled data more effectively since they either use a
pre-defined / fixed threshold or an ad-hoc threshold adjusting scheme,
resulting in inferior performance and slow convergence. We first analyze a
motivating example to obtain intuitions on the relationship between the
desirable threshold and model's learning status. Based on the analysis, we
hence propose FreeMatch to adjust the confidence threshold in a self-adaptive
manner according to the model's learning status. We further introduce a
self-adaptive class fairness regularization penalty to encourage the model for
diverse predictions during the early training stage. Extensive experiments
indicate the superiority of FreeMatch especially when the labeled data are
extremely rare. FreeMatch achieves 5.78%, 13.59%, and 1.28% error rate
reduction over the latest state-of-the-art method FlexMatch on CIFAR-10 with 1
label per class, STL-10 with 4 labels per class, and ImageNet with 100 labels
per class, respectively. Moreover, FreeMatch can also boost the performance of
imbalanced SSL. The codes can be found at
https://github.com/microsoft/Semi-supervised-learning.
| true | true |
Yidong Wang and
Hao Chen and
Qiang Heng and
Wenxin Hou and
Yue Fan and
Zhen Wu and
Jindong Wang and
Marios Savvides and
Takahiro Shinozaki and
Bhiksha Raj and
Bernt Schiele and
Xing Xie
| 2,023 | null | null | null | null |
FreeMatch: Self-adaptive Thresholding for Semi-supervised Learning
|
FreeMatch: Self-adaptive Thresholding for Semi-supervised Learning
|
https://openreview.net/forum?id=PDrUPTXJI_A
|
We propose FreeMatch to define and adjust the confidence threshold in a self-adaptive manner for semi-supervised learning.
|
Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised
Learning with Outliers
|
2505.24443v1
|
ssl_8
|
\cite{ssl_8}
|
SoftMatch: Addressing the Quantity-Quality Trade-off in Semi-supervised
Learning
|
http://arxiv.org/abs/2301.10921v2
|
The critical challenge of Semi-Supervised Learning (SSL) is how to
effectively leverage the limited labeled data and massive unlabeled data to
improve the model's generalization performance. In this paper, we first revisit
the popular pseudo-labeling methods via a unified sample weighting formulation
and demonstrate the inherent quantity-quality trade-off problem of
pseudo-labeling with thresholding, which may prohibit learning. To this end, we
propose SoftMatch to overcome the trade-off by maintaining both high quantity
and high quality of pseudo-labels during training, effectively exploiting the
unlabeled data. We derive a truncated Gaussian function to weight samples based
on their confidence, which can be viewed as a soft version of the confidence
threshold. We further enhance the utilization of weakly-learned classes by
proposing a uniform alignment approach. In experiments, SoftMatch shows
substantial improvements across a wide variety of benchmarks, including image,
text, and imbalanced classification.
| true | true |
Hao Chen and Ran Tao and Yue Fan and Yidong Wang and Jindong Wang and Bernt Schiele and Xing Xie and Bhiksha Raj and Marios Savvides
| 2,023 | null | null | null | null |
SoftMatch: Addressing the Quantity-Quality Trade-off in Semi-supervised
Learning
|
Addressing the Quantity-Quality Tradeoff in Semi-supervised Learning
|
https://openreview.net/forum?id=ymt1zQXBDiF
|
This paper proposes SoftMatch to improve both the quantity and quality of pseudo-labels in semi-supervised learning. Basically, the authors
|
Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised
Learning with Outliers
|
2505.24443v1
|
ssl_6
|
\cite{ssl_6}
|
SimMatch: Semi-supervised Learning with Similarity Matching
|
http://arxiv.org/abs/2203.06915v2
|
Learning with few labeled data has been a longstanding problem in the
computer vision and machine learning research community. In this paper, we
introduced a new semi-supervised learning framework, SimMatch, which
simultaneously considers semantic similarity and instance similarity. In
SimMatch, the consistency regularization will be applied on both semantic-level
and instance-level. The different augmented views of the same instance are
encouraged to have the same class prediction and similar similarity
relationship respected to other instances. Next, we instantiated a labeled
memory buffer to fully leverage the ground truth labels on instance-level and
bridge the gaps between the semantic and instance similarities. Finally, we
proposed the \textit{unfolding} and \textit{aggregation} operation which allows
these two similarities be isomorphically transformed with each other. In this
way, the semantic and instance pseudo-labels can be mutually propagated to
generate more high-quality and reliable matching targets. Extensive
experimental results demonstrate that SimMatch improves the performance of
semi-supervised learning tasks across different benchmark datasets and
different settings. Notably, with 400 epochs of training, SimMatch achieves
67.2\%, and 74.4\% Top-1 Accuracy with 1\% and 10\% labeled examples on
ImageNet, which significantly outperforms the baseline methods and is better
than previous semi-supervised learning frameworks. Code and pre-trained models
are available at https://github.com/KyleZheng1997/simmatch.
| true | true |
Mingkai Zheng and
Shan You and
Lang Huang and
Fei Wang and
Chen Qian and
Chang Xu
| 2,022 | null | null | null | null |
SimMatch: Semi-supervised Learning with Similarity Matching
|
SimMatch: Semi-supervised Learning with Similarity ...
|
https://arxiv.org/abs/2203.06915
|
by M Zheng · 2022 · Cited by 309 — In this paper, we introduced a new semi-supervised learning framework, SimMatch, which simultaneously considers semantic similarity and instance similarity.
|
Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised
Learning with Outliers
|
2505.24443v1
|
ssl_7
|
\cite{ssl_7}
|
SimMatchV2: Semi-Supervised Learning with Graph Consistency
|
http://arxiv.org/abs/2308.06692v1
|
Semi-Supervised image classification is one of the most fundamental problem
in computer vision, which significantly reduces the need for human labor. In
this paper, we introduce a new semi-supervised learning algorithm - SimMatchV2,
which formulates various consistency regularizations between labeled and
unlabeled data from the graph perspective. In SimMatchV2, we regard the
augmented view of a sample as a node, which consists of a label and its
corresponding representation. Different nodes are connected with the edges,
which are measured by the similarity of the node representations. Inspired by
the message passing and node classification in graph theory, we propose four
types of consistencies, namely 1) node-node consistency, 2) node-edge
consistency, 3) edge-edge consistency, and 4) edge-node consistency. We also
uncover that a simple feature normalization can reduce the gaps of the feature
norm between different augmented views, significantly improving the performance
of SimMatchV2. Our SimMatchV2 has been validated on multiple semi-supervised
learning benchmarks. Notably, with ResNet-50 as our backbone and 300 epochs of
training, SimMatchV2 achieves 71.9\% and 76.2\% Top-1 Accuracy with 1\% and
10\% labeled examples on ImageNet, which significantly outperforms the previous
methods and achieves state-of-the-art performance. Code and pre-trained models
are available at
\href{https://github.com/mingkai-zheng/SimMatchV2}{https://github.com/mingkai-zheng/SimMatchV2}.
| true | true |
Mingkai Zheng and
Shan You and
Lang Huang and
Chen Luo and
Fei Wang and
Chen Qian and
Chang Xu
| 2,023 | null | null | null | null |
SimMatchV2: Semi-Supervised Learning with Graph Consistency
|
Semi-Supervised Learning with Graph Consistency
|
https://arxiv.org/abs/2308.06692
|
by M Zheng · 2023 · Cited by 17 — In this paper, we introduce a new semi-supervised learning algorithm - SimMatchV2, which formulates various consistency regularizations between labeled and
|
Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised
Learning with Outliers
|
2505.24443v1
|
ssl_17
|
\cite{ssl_17}
|
Label Propagation for Deep Semi-supervised Learning
|
http://arxiv.org/abs/1904.04717v1
|
Semi-supervised learning is becoming increasingly important because it can
combine data carefully labeled by humans with abundant unlabeled data to train
deep neural networks. Classic methods on semi-supervised learning that have
focused on transductive learning have not been fully exploited in the inductive
framework followed by modern deep learning. The same holds for the manifold
assumption---that similar examples should get the same prediction. In this
work, we employ a transductive label propagation method that is based on the
manifold assumption to make predictions on the entire dataset and use these
predictions to generate pseudo-labels for the unlabeled data and train a deep
neural network. At the core of the transductive method lies a nearest neighbor
graph of the dataset that we create based on the embeddings of the same
network.Therefore our learning process iterates between these two steps. We
improve performance on several datasets especially in the few labels regime and
show that our work is complementary to current state of the art.
| true | true |
Ahmet Iscen and
Giorgos Tolias and
Yannis Avrithis and
Ondrej Chum
| 2,019 | null | null | null | null |
Label Propagation for Deep Semi-supervised Learning
|
[PDF] Label Propagation for Deep Semi-Supervised Learning
|
https://openaccess.thecvf.com/content_CVPR_2019/papers/Iscen_Label_Propagation_for_Deep_Semi-Supervised_Learning_CVPR_2019_paper.pdf
|
Label propagation uses a transductive method to generate pseudo-labels for unlabeled data, using a graph based on network embeddings, to train a deep neural
|
Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised
Learning with Outliers
|
2505.24443v1
|
tnnls_3
|
\cite{tnnls_3}
|
Graph-Based Semi-Supervised Learning: {A} Comprehensive Review
| null | null | true | false |
Zixing Song and
Xiangli Yang and
Zenglin Xu and
Irwin King
| 2,023 | null | null | null |
{IEEE} Trans. on Neural Networks and Learning Systems
|
Graph-Based Semi-Supervised Learning: {A} Comprehensive Review
|
Graph-Based Semi-Supervised Learning
|
https://ieeexplore.ieee.org/document/9737635
|
Graph-Based Semi-Supervised Learning: A Comprehensive Review | IEEE Journals & Magazine | IEEE Xplore Publisher: IEEE An essential class of SSL methods, referred to as graph-based semi-supervised learning (GSSL) methods in the literature, is to first represent each sample as a node in an affinity graph, and then, the label information of unlabeled samples can be inferred based on the structure of the constructed graph. Publisher: IEEE A similarity graph is constructed based on the given data, including both the labeled and unlabeled samples. Image 4: Contact IEEE to Subscribe About IEEE _Xplore_ | Contact Us | Help | Accessibility | Terms of Use | Nondiscrimination Policy | IEEE Ethics Reporting | Sitemap | IEEE Privacy Policy
|
Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised
Learning with Outliers
|
2505.24443v1
|
ssl_5
|
\cite{ssl_5}
|
CoMatch: Semi-supervised Learning with Contrastive Graph Regularization
|
http://arxiv.org/abs/2011.11183v2
|
Semi-supervised learning has been an effective paradigm for leveraging
unlabeled data to reduce the reliance on labeled data. We propose CoMatch, a
new semi-supervised learning method that unifies dominant approaches and
addresses their limitations. CoMatch jointly learns two representations of the
training data, their class probabilities and low-dimensional embeddings. The
two representations interact with each other to jointly evolve. The embeddings
impose a smoothness constraint on the class probabilities to improve the
pseudo-labels, whereas the pseudo-labels regularize the structure of the
embeddings through graph-based contrastive learning. CoMatch achieves
state-of-the-art performance on multiple datasets. It achieves substantial
accuracy improvements on the label-scarce CIFAR-10 and STL-10. On ImageNet with
1% labels, CoMatch achieves a top-1 accuracy of 66.0%, outperforming FixMatch
by 12.6%. Furthermore, CoMatch achieves better representation learning
performance on downstream tasks, outperforming both supervised learning and
self-supervised learning. Code and pre-trained models are available at
https://github.com/salesforce/CoMatch.
| true | true |
Junnan Li and
Caiming Xiong and
Steven C. H. Hoi
| 2,021 | null | null | null | null |
CoMatch: Semi-supervised Learning with Contrastive Graph Regularization
|
CoMatch: Semi-Supervised Learning With Contrastive ...
|
https://openaccess.thecvf.com/content/ICCV2021/papers/Li_CoMatch_Semi-Supervised_Learning_With_Contrastive_Graph_Regularization_ICCV_2021_paper.pdf
|
by J Li · 2021 · Cited by 384 — We propose CoMatch, a new semi-supervised learning method that unifies dominant approaches and addresses their limitations. CoMatch jointly learns two
|
Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised
Learning with Outliers
|
2505.24443v1
|
rep_3
|
\cite{rep_3}
|
Big Self-Supervised Models are Strong Semi-Supervised Learners
|
http://arxiv.org/abs/2006.10029v2
|
One paradigm for learning from few labeled examples while making best use of
a large amount of unlabeled data is unsupervised pretraining followed by
supervised fine-tuning. Although this paradigm uses unlabeled data in a
task-agnostic way, in contrast to common approaches to semi-supervised learning
for computer vision, we show that it is surprisingly effective for
semi-supervised learning on ImageNet. A key ingredient of our approach is the
use of big (deep and wide) networks during pretraining and fine-tuning. We find
that, the fewer the labels, the more this approach (task-agnostic use of
unlabeled data) benefits from a bigger network. After fine-tuning, the big
network can be further improved and distilled into a much smaller one with
little loss in classification accuracy by using the unlabeled examples for a
second time, but in a task-specific way. The proposed semi-supervised learning
algorithm can be summarized in three steps: unsupervised pretraining of a big
ResNet model using SimCLRv2, supervised fine-tuning on a few labeled examples,
and distillation with unlabeled examples for refining and transferring the
task-specific knowledge. This procedure achieves 73.9% ImageNet top-1 accuracy
with just 1% of the labels ($\le$13 labeled images per class) using ResNet-50,
a $10\times$ improvement in label efficiency over the previous
state-of-the-art. With 10% of labels, ResNet-50 trained with our method
achieves 77.5% top-1 accuracy, outperforming standard supervised training with
all of the labels.
| true | true |
Ting Chen and
Simon Kornblith and
Kevin Swersky and
Mohammad Norouzi and
Geoffrey E. Hinton
| 2,020 | null | null | null | null |
Big Self-Supervised Models are Strong Semi-Supervised Learners
|
[2006.10029] Big Self-Supervised Models are Strong Semi ...
|
https://arxiv.org/abs/2006.10029
|
by T Chen · 2020 · Cited by 2883 — We show that it is surprisingly effective for semi-supervised learning on ImageNet. A key ingredient of our approach is the use of big (deep and wide) networks.
|
Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised
Learning with Outliers
|
2505.24443v1
|
ssl_1
|
\cite{ssl_1}
|
Realistic Evaluation of Deep Semi-Supervised Learning Algorithms
|
http://arxiv.org/abs/1804.09170v4
|
Semi-supervised learning (SSL) provides a powerful framework for leveraging
unlabeled data when labels are limited or expensive to obtain. SSL algorithms
based on deep neural networks have recently proven successful on standard
benchmark tasks. However, we argue that these benchmarks fail to address many
issues that these algorithms would face in real-world applications. After
creating a unified reimplementation of various widely-used SSL techniques, we
test them in a suite of experiments designed to address these issues. We find
that the performance of simple baselines which do not use unlabeled data is
often underreported, that SSL methods differ in sensitivity to the amount of
labeled and unlabeled data, and that performance can degrade substantially when
the unlabeled dataset contains out-of-class examples. To help guide SSL
research towards real-world applicability, we make our unified reimplemention
and evaluation platform publicly available.
| true | true |
Avital Oliver and
Augustus Odena and
Colin Raffel and
Ekin Dogus Cubuk and
Ian J. Goodfellow
| 2,018 | null | null | null | null |
Realistic Evaluation of Deep Semi-Supervised Learning Algorithms
|
Realistic Evaluation of Deep Semi-Supervised Learning Algorithms
|
https://arxiv.org/abs/1804.09170
|
Semi-supervised learning (SSL) provides a powerful framework for leveraging unlabeled data when labels are limited or expensive to obtain.
|
Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised
Learning with Outliers
|
2505.24443v1
|
ossl_2
|
\cite{ossl_2}
|
Semi-Supervised Learning under Class Distribution Mismatch
| null | null | true | false |
Yanbei Chen and
Xiatian Zhu and
Wei Li and
Shaogang Gong
| 2,020 | null | null | null | null |
Semi-Supervised Learning under Class Distribution Mismatch
|
[PDF] Semi-Supervised Learning under Class Distribution Mismatch
|
https://ojs.aaai.org/index.php/AAAI/article/view/5763/5619
|
Class distribution mismatch in semi-supervised learning occurs when labeled and unlabeled data come from different class distributions, unlike conventional SSL.
|
Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised
Learning with Outliers
|
2505.24443v1
|
ossl_14
|
\cite{ossl_14}
|
SCOMatch: Alleviating Overtrusting in Open-set Semi-supervised Learning
|
http://arxiv.org/abs/2409.17512v1
|
Open-set semi-supervised learning (OSSL) leverages practical open-set
unlabeled data, comprising both in-distribution (ID) samples from seen classes
and out-of-distribution (OOD) samples from unseen classes, for semi-supervised
learning (SSL). Prior OSSL methods initially learned the decision boundary
between ID and OOD with labeled ID data, subsequently employing self-training
to refine this boundary. These methods, however, suffer from the tendency to
overtrust the labeled ID data: the scarcity of labeled data caused the
distribution bias between the labeled samples and the entire ID data, which
misleads the decision boundary to overfit. The subsequent self-training
process, based on the overfitted result, fails to rectify this problem. In this
paper, we address the overtrusting issue by treating OOD samples as an
additional class, forming a new SSL process.
Specifically, we propose SCOMatch, a novel OSSL method that 1) selects
reliable OOD samples as new labeled data with an OOD memory queue and a
corresponding update strategy and 2) integrates the new SSL process into the
original task through our Simultaneous Close-set and Open-set self-training.
SCOMatch refines the decision boundary of ID and OOD classes across the entire
dataset, thereby leading to improved results. Extensive experimental results
show that SCOMatch significantly outperforms the state-of-the-art methods on
various benchmarks. The effectiveness is further verified through ablation
studies and visualization.
| true | true |
Wang, Zerun and Xiang, Liuyu and Huang, Lang and Mao, Jiafeng and Xiao, Ling and Yamasaki, Toshihiko
| 2,025 | null | null | null | null |
SCOMatch: Alleviating Overtrusting in Open-set Semi-supervised Learning
|
Alleviating Overtrusting in Open-set Semi-supervised Learning - arXiv
|
https://arxiv.org/abs/2409.17512
|
We propose SCOMatch, a novel OSSL method that 1) selects reliable OOD samples as new labeled data with an OOD memory queue and a corresponding update strategy.
|
Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised
Learning with Outliers
|
2505.24443v1
|
ossl_12
|
\cite{ossl_12}
|
Rethinking safe semi-supervised learning: Transferring the open-set problem to a close-set one
| null | null | true | false |
Ma, Qiankun and Gao, Jiyao and Zhan, Bo and Guo, Yunpeng and Zhou, Jiliu and Wang, Yan
| 2,023 | null | null | null | null |
Rethinking safe semi-supervised learning: Transferring the open-set problem to a close-set one
|
[PDF] Rethinking Safe Semi-supervised Learning - CVF Open Access
|
https://openaccess.thecvf.com/content/ICCV2023/supplemental/Ma_Rethinking_Safe_Semi-supervised_ICCV_2023_supplemental.pdf
|
Page 1. Rethinking Safe Semi-supervised Learning: Transferring the Open-set Problem to A Close-set One. -Supplementary Material-. 1. Detailed Datasets. In this
|
Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised
Learning with Outliers
|
2505.24443v1
|
ossl_16
|
\cite{ossl_16}
|
Semi-Supervised Learning via Weight-aware Distillation under Class
Distribution Mismatch
|
http://arxiv.org/abs/2308.11874v1
|
Semi-Supervised Learning (SSL) under class distribution mismatch aims to
tackle a challenging problem wherein unlabeled data contain lots of unknown
categories unseen in the labeled ones. In such mismatch scenarios, traditional
SSL suffers severe performance damage due to the harmful invasion of the
instances with unknown categories into the target classifier. In this study, by
strict mathematical reasoning, we reveal that the SSL error under class
distribution mismatch is composed of pseudo-labeling error and invasion error,
both of which jointly bound the SSL population risk. To alleviate the SSL
error, we propose a robust SSL framework called Weight-Aware Distillation (WAD)
that, by weights, selectively transfers knowledge beneficial to the target task
from unsupervised contrastive representation to the target classifier.
Specifically, WAD captures adaptive weights and high-quality pseudo labels to
target instances by exploring point mutual information (PMI) in representation
space to maximize the role of unlabeled data and filter unknown categories.
Theoretically, we prove that WAD has a tight upper bound of population risk
under class distribution mismatch. Experimentally, extensive results
demonstrate that WAD outperforms five state-of-the-art SSL approaches and one
standard baseline on two benchmark datasets, CIFAR10 and CIFAR100, and an
artificial cross-dataset. The code is available at
https://github.com/RUC-DWBI-ML/research/tree/main/WAD-master.
| true | true |
Du, Pan and Zhao, Suyun and Sheng, Zisen and Li, Cuiping and Chen, Hong
| 2,023 | null | null | null | null |
Semi-Supervised Learning via Weight-aware Distillation under Class
Distribution Mismatch
|
Semi-Supervised Learning via Weight-Aware Distillation ...
|
https://openaccess.thecvf.com/content/ICCV2023/papers/Du_Semi-Supervised_Learning_via_Weight-Aware_Distillation_under_Class_Distribution_Mismatch_ICCV_2023_paper.pdf
|
by P Du · 2023 · Cited by 11 — Semi-Supervised Learning (SSL) under class distribu- tion mismatch aims to tackle a challenging problem wherein unlabeled data contain lots of unknown
|
Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised
Learning with Outliers
|
2505.24443v1
|
ossl_5
|
\cite{ossl_5}
|
Safe-Student for Safe Deep Semi-Supervised Learning with Unseen-Class
Unlabeled Data
| null | null | true | false |
Rundong He and
Zhongyi Han and
Xiankai Lu and
Yilong Yin
| 2,022 | null | null | null | null |
Safe-Student for Safe Deep Semi-Supervised Learning with Unseen-Class
Unlabeled Data
|
SAFER-STUDENT for Safe Deep Semi-Supervised Learning With...
|
https://openreview.net/forum?id=j8i42Lrh0Z
|
Missing: 04/08/2025
|
Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised
Learning with Outliers
|
2505.24443v1
|
ossl_6
|
\cite{ossl_6}
|
{SAFER-STUDENT} for Safe Deep Semi-Supervised Learning With Unseen-Class
Unlabeled Data
| null | null | true | false |
Rundong He and
Zhongyi Han and
Xiankai Lu and
Yilong Yin
| 2,024 | null | null | null |
{IEEE} Trans. on Knowledge and Data Engineering
|
{SAFER-STUDENT} for Safe Deep Semi-Supervised Learning With Unseen-Class
Unlabeled Data
|
SAFER-STUDENT for Safe Deep Semi-Supervised Learning With ...
|
https://www.researchgate.net/publication/371000311_SAFER-STUDENT_for_Safe_Deep_Semi-Supervised_Learning_With_Unseen-Class_Unlabeled_Data
|
Deep semi-supervised learning (SSL) methods aim to utilize abundant unlabeled data to improve the seen-class classification. Several similar definitions have emerged to describe this scenario, including safe SSL [9], open-set SSL [22,24,31,45], and the challenge of managing UnLabeled data from Unseen Classes in Semi-Supervised Learning (ULUC-SSL) [14]. In particular, we note that existing open-set SSL methods rely on prediction discrepancies between inliers and outliers from a single model trained on labeled data. To effectively alleviate the SVA data labeling cost, we propose an approach SURF, which makes full use of a limited amount of labeled SVA data combined with a large amount of unlabeled SVA data to train the SVA model via semi-supervised learning.
|
Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised
Learning with Outliers
|
2505.24443v1
|
ossl_3
|
\cite{ossl_3}
|
Multi-Task Curriculum Framework for Open-Set Semi-Supervised Learning
|
http://arxiv.org/abs/2007.11330v1
|
Semi-supervised learning (SSL) has been proposed to leverage unlabeled data
for training powerful models when only limited labeled data is available. While
existing SSL methods assume that samples in the labeled and unlabeled data
share the classes of their samples, we address a more complex novel scenario
named open-set SSL, where out-of-distribution (OOD) samples are contained in
unlabeled data. Instead of training an OOD detector and SSL separately, we
propose a multi-task curriculum learning framework. First, to detect the OOD
samples in unlabeled data, we estimate the probability of the sample belonging
to OOD. We use a joint optimization framework, which updates the network
parameters and the OOD score alternately. Simultaneously, to achieve high
performance on the classification of in-distribution (ID) data, we select ID
samples in unlabeled data having small OOD scores, and use these data with
labeled data for training the deep neural networks to classify ID samples in a
semi-supervised manner. We conduct several experiments, and our method achieves
state-of-the-art results by successfully eliminating the effect of OOD samples.
| true | true |
Qing Yu and
Daiki Ikami and
Go Irie and
Kiyoharu Aizawa
| 2,020 | null | null | null | null |
Multi-Task Curriculum Framework for Open-Set Semi-Supervised Learning
|
YU1ut/Multi-Task-Curriculum-Framework-for-Open-Set-SSL
|
https://github.com/YU1ut/Multi-Task-Curriculum-Framework-for-Open-Set-SSL
|
This is the official PyTorch implementation of Multi-Task Curriculum Framework for Open-Set Semi-Supervised Learning. architecture. Requirements. Python 3.7
|
Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised
Learning with Outliers
|
2505.24443v1
|
ossl_9
|
\cite{ossl_9}
|
Trash to Treasure: Harvesting OOD Data with Cross-Modal Matching for
Open-Set Semi-Supervised Learning
|
http://arxiv.org/abs/2108.05617v1
|
Open-set semi-supervised learning (open-set SSL) investigates a challenging
but practical scenario where out-of-distribution (OOD) samples are contained in
the unlabeled data. While the mainstream technique seeks to completely filter
out the OOD samples for semi-supervised learning (SSL), we propose a novel
training mechanism that could effectively exploit the presence of OOD data for
enhanced feature learning while avoiding its adverse impact on the SSL. We
achieve this goal by first introducing a warm-up training that leverages all
the unlabeled data, including both the in-distribution (ID) and OOD samples.
Specifically, we perform a pretext task that enforces our feature extractor to
obtain a high-level semantic understanding of the training images, leading to
more discriminative features that can benefit the downstream tasks. Since the
OOD samples are inevitably detrimental to SSL, we propose a novel cross-modal
matching strategy to detect OOD samples. Instead of directly applying binary
classification, we train the network to predict whether the data sample is
matched to an assigned one-hot class label. The appeal of the proposed
cross-modal matching over binary classification is the ability to generate a
compatible feature space that aligns with the core classification task.
Extensive experiments show that our approach substantially lifts the
performance on open-set SSL and outperforms the state-of-the-art by a large
margin.
| true | true |
Junkai Huang and
Chaowei Fang and
Weikai Chen and
Zhenhua Chai and
Xiaolin Wei and
Pengxu Wei and
Liang Lin and
Guanbin Li
| 2,021 | null | null | null | null |
Trash to Treasure: Harvesting OOD Data with Cross-Modal Matching for
Open-Set Semi-Supervised Learning
|
[PDF] Harvesting OOD Data With Cross-Modal Matching for Open-Set ...
|
https://guanbinli.com/papers/4-Huang_Trash_To_Treasure_Harvesting_OOD_Data_With_Cross-Modal_Matching_for_ICCV_2021_paper.pdf
|
Open-set semi-supervised learning (open-set SSL) inves- tigates a challenging but practical scenario where out-of- distribution (OOD) samples are contained
|
Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised
Learning with Outliers
|
2505.24443v1
|
ossl_10
|
\cite{ossl_10}
|
Out-of-Distributed Semantic Pruning for Robust Semi-Supervised Learning
|
http://arxiv.org/abs/2305.18158v2
|
Recent advances in robust semi-supervised learning (SSL) typically filter
out-of-distribution (OOD) information at the sample level. We argue that an
overlooked problem of robust SSL is its corrupted information on semantic
level, practically limiting the development of the field. In this paper, we
take an initial step to explore and propose a unified framework termed OOD
Semantic Pruning (OSP), which aims at pruning OOD semantics out from
in-distribution (ID) features. Specifically, (i) we propose an aliasing OOD
matching module to pair each ID sample with an OOD sample with semantic
overlap. (ii) We design a soft orthogonality regularization, which first
transforms each ID feature by suppressing its semantic component that is
collinear with paired OOD sample. It then forces the predictions before and
after soft orthogonality decomposition to be consistent. Being practically
simple, our method shows a strong performance in OOD detection and ID
classification on challenging benchmarks. In particular, OSP surpasses the
previous state-of-the-art by 13.7% on accuracy for ID classification and 5.9%
on AUROC for OOD detection on TinyImageNet dataset. The source codes are
publicly available at https://github.com/rain305f/OSP.
| true | true |
Wang, Yu and Qiao, Pengchong and Liu, Chang and Song, Guoli and Zheng, Xiawu and Chen, Jie
| 2,023 | null | null | null | null |
Out-of-Distributed Semantic Pruning for Robust Semi-Supervised Learning
|
[PDF] Out-of-Distributed Semantic Pruning for Robust Semi-Supervised ...
|
https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_Out-of-Distributed_Semantic_Pruning_for_Robust_Semi-Supervised_Learning_CVPR_2023_paper.pdf
|
Recent advances in robust semi-supervised learning. (SSL) typically filter out-of-distribution (OOD) information at the sample level.
|
Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised
Learning with Outliers
|
2505.24443v1
|
ossl_8
|
\cite{ossl_8}
|
Unknown-Aware Graph Regularization for Robust Semi-supervised Learning
from Uncurated Data
| null | null | true | false |
Heejo Kong and
Suneung Kim and
Ho{-}Joong Kim and
Seong{-}Whan Lee
| 2,024 | null | null | null | null |
Unknown-Aware Graph Regularization for Robust Semi-supervised Learning
from Uncurated Data
|
Unknown-Aware Graph Regularization for Robust Semi- ...
|
https://www.researchgate.net/publication/379297624_Unknown-Aware_Graph_Regularization_for_Robust_Semi-supervised_Learning_from_Uncurated_Data
|
In this paper, we propose a robust SSL method for learning from uncurated real-world data within the context of open-set semi-supervised learning (OSSL). Unlike
|
Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised
Learning with Outliers
|
2505.24443v1
|
ossl_4
|
\cite{ossl_4}
|
OpenMatch: Open-set Consistency Regularization for Semi-supervised
Learning with Outliers
|
http://arxiv.org/abs/2105.14148v2
|
Semi-supervised learning (SSL) is an effective means to leverage unlabeled
data to improve a model's performance. Typical SSL methods like FixMatch assume
that labeled and unlabeled data share the same label space. However, in
practice, unlabeled data can contain categories unseen in the labeled set,
i.e., outliers, which can significantly harm the performance of SSL algorithms.
To address this problem, we propose a novel Open-set Semi-Supervised Learning
(OSSL) approach called OpenMatch. Learning representations of inliers while
rejecting outliers is essential for the success of OSSL. To this end, OpenMatch
unifies FixMatch with novelty detection based on one-vs-all (OVA) classifiers.
The OVA-classifier outputs the confidence score of a sample being an inlier,
providing a threshold to detect outliers. Another key contribution is an
open-set soft-consistency regularization loss, which enhances the smoothness of
the OVA-classifier with respect to input transformations and greatly improves
outlier detection. OpenMatch achieves state-of-the-art performance on three
datasets, and even outperforms a fully supervised model in detecting outliers
unseen in unlabeled data on CIFAR10.
| true | true |
Saito, Kuniaki and Kim, Donghyun and Saenko, Kate
| 2,021 | null | null | null | null |
OpenMatch: Open-set Consistency Regularization for Semi-supervised
Learning with Outliers
|
VisionLearningGroup/OP_Match
|
https://github.com/VisionLearningGroup/OP_Match
|
OpenMatch: Open-set Consistency Regularization for Semi-supervised Learning with Outliers (NeurIPS 2021) ... This is an PyTorch implementation of OpenMatch. This
|
Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised
Learning with Outliers
|
2505.24443v1
|
ossl_7
|
\cite{ossl_7}
|
IOMatch: Simplifying Open-Set Semi-Supervised Learning with Joint
Inliers and Outliers Utilization
|
http://arxiv.org/abs/2308.13168v1
|
Semi-supervised learning (SSL) aims to leverage massive unlabeled data when
labels are expensive to obtain. Unfortunately, in many real-world applications,
the collected unlabeled data will inevitably contain unseen-class outliers not
belonging to any of the labeled classes. To deal with the challenging open-set
SSL task, the mainstream methods tend to first detect outliers and then filter
them out. However, we observe a surprising fact that such approach could result
in more severe performance degradation when labels are extremely scarce, as the
unreliable outlier detector may wrongly exclude a considerable portion of
valuable inliers. To tackle with this issue, we introduce a novel open-set SSL
framework, IOMatch, which can jointly utilize inliers and outliers, even when
it is difficult to distinguish exactly between them. Specifically, we propose
to employ a multi-binary classifier in combination with the standard closed-set
classifier for producing unified open-set classification targets, which regard
all outliers as a single new class. By adopting these targets as open-set
pseudo-labels, we optimize an open-set classifier with all unlabeled samples
including both inliers and outliers. Extensive experiments have shown that
IOMatch significantly outperforms the baseline methods across different
benchmark datasets and different settings despite its remarkable simplicity.
Our code and models are available at https://github.com/nukezil/IOMatch.
| true | true |
Zekun Li and
Lei Qi and
Yinghuan Shi and
Yang Gao
| 2,023 | null | null | null | null |
IOMatch: Simplifying Open-Set Semi-Supervised Learning with Joint
Inliers and Outliers Utilization
|
[ICCV 2023 Oral] IOMatch: Simplifying Open-Set Semi-Supervised ...
|
https://github.com/nukezil/IOMatch
|
This is the official repository for our ICCV 2023 paper: IOMatch: Simplifying Open-Set Semi-Supervised Learning with Joint Inliers and Outliers Utilization.
|
Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised
Learning with Outliers
|
2505.24443v1
|
ossl_11
|
\cite{ossl_11}
|
SSB: Simple but Strong Baseline for Boosting Performance of Open-Set
Semi-Supervised Learning
|
http://arxiv.org/abs/2311.10572v1
|
Semi-supervised learning (SSL) methods effectively leverage unlabeled data to
improve model generalization. However, SSL models often underperform in
open-set scenarios, where unlabeled data contain outliers from novel categories
that do not appear in the labeled set. In this paper, we study the challenging
and realistic open-set SSL setting, where the goal is to both correctly
classify inliers and to detect outliers. Intuitively, the inlier classifier
should be trained on inlier data only. However, we find that inlier
classification performance can be largely improved by incorporating
high-confidence pseudo-labeled data, regardless of whether they are inliers or
outliers. Also, we propose to utilize non-linear transformations to separate
the features used for inlier classification and outlier detection in the
multi-task learning framework, preventing adverse effects between them.
Additionally, we introduce pseudo-negative mining, which further boosts outlier
detection performance. The three ingredients lead to what we call Simple but
Strong Baseline (SSB) for open-set SSL. In experiments, SSB greatly improves
both inlier classification and outlier detection performance, outperforming
existing methods by a large margin. Our code will be released at
https://github.com/YUE-FAN/SSB.
| true | true |
Fan, Yue and Kukleva, Anna and Dai, Dengxin and Schiele, Bernt
| 2,023 | null | null | null | null |
SSB: Simple but Strong Baseline for Boosting Performance of Open-Set
Semi-Supervised Learning
|
SSB: Simple but Strong Baseline for Boosting Performance ...
|
https://ieeexplore.ieee.org/iel7/10376473/10376477/10377450.pdf
|
by Y Fan · 2023 · Cited by 17 — Semi-supervised learning. (SSL) aims to improve model performance by exploiting both labeled and unlabeled data. As one of the most widely used techniques,
|
Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised
Learning with Outliers
|
2505.24443v1
|
ossl_1
|
\cite{ossl_1}
|
Safe Deep Semi-Supervised Learning for Unseen-Class Unlabeled Data
| null | null | true | false |
Lan{-}Zhe Guo and
Zhenyu Zhang and
Yuan Jiang and
Yufeng Li and
Zhi{-}Hua Zhou
| 2,020 | null | null | null | null |
Safe Deep Semi-Supervised Learning for Unseen-Class Unlabeled Data
|
[PDF] Safe Deep Semi-Supervised Learning for Unseen-Class Unlabeled ...
|
http://proceedings.mlr.press/v119/guo20i/guo20i.pdf
|
Deep semi-supervised learning (SSL) is proposed to uti- lize a large number of cheap unlabeled data to help deep neural networks improve performance, reducing
|
Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised
Learning with Outliers
|
2505.24443v1
|
ossl_13
|
\cite{ossl_13}
|
Binary Decomposition: A Problem Transformation Perspective for Open-Set Semi-Supervised Learning
| null | null | true | false |
Hang, Jun-Yi and Zhang, Min-Ling
| 2,024 | null | null | null | null |
Binary Decomposition: A Problem Transformation Perspective for Open-Set Semi-Supervised Learning
|
Binary decomposition | Proceedings of the 41st International ...
|
https://dl.acm.org/doi/10.5555/3692070.3692767
|
Binary decomposition: a problem transformation perspective for open-set semi-supervised learning. Computing methodologies · Machine learning.
|
Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised
Learning with Outliers
|
2505.24443v1
|
ossl_17
|
\cite{ossl_17}
|
They are Not Completely Useless: Towards Recycling Transferable
Unlabeled Data for Class-Mismatched Semi-Supervised Learning
|
http://arxiv.org/abs/2011.13529v4
|
Semi-Supervised Learning (SSL) with mismatched classes deals with the problem
that the classes-of-interests in the limited labeled data is only a subset of
the classes in massive unlabeled data. As a result, the classes only possessed
by the unlabeled data may mislead the classifier training and thus hindering
the realistic landing of various SSL methods. To solve this problem, existing
methods usually divide unlabeled data to in-distribution (ID) data and
out-of-distribution (OOD) data, and directly discard or weaken the OOD data to
avoid their adverse impact. In other words, they treat OOD data as completely
useless and thus the potential valuable information for classification
contained by them is totally ignored. To remedy this defect, this paper
proposes a "Transferable OOD data Recycling" (TOOR) method which properly
utilizes ID data as well as the "recyclable" OOD data to enrich the information
for conducting class-mismatched SSL. Specifically, TOOR firstly attributes all
unlabeled data to ID data or OOD data, among which the ID data are directly
used for training. Then we treat the OOD data that have a close relationship
with ID data and labeled data as recyclable, and employ adversarial domain
adaptation to project them to the space of ID data and labeled data. In other
words, the recyclability of an OOD datum is evaluated by its transferability,
and the recyclable OOD data are transferred so that they are compatible with
the distribution of known classes-of-interests. Consequently, our TOOR method
extracts more information from unlabeled data than existing approaches, so it
can achieve the improved performance which is demonstrated by the experiments
on typical benchmark datasets.
| true | true |
Huang, Zhuo and Yang, Jian and Gong, Chen
| 2,022 | null | null | null |
{IEEE} Trans. on Multimedia
|
They are Not Completely Useless: Towards Recycling Transferable
Unlabeled Data for Class-Mismatched Semi-Supervised Learning
|
Towards Recycling Transferable Unlabeled Data for Class ... - arXiv
|
https://arxiv.org/abs/2011.13529
|
They are Not Completely Useless: Towards Recycling Transferable Unlabeled Data for Class-Mismatched Semi-Supervised Learning. Authors:Zhuo Huang
|
KairosAD: A SAM-Based Model for Industrial Anomaly Detection on Embedded
Devices
|
2505.24334v1
|
liu2024deep
|
\cite{liu2024deep}
|
Deep Industrial Image Anomaly Detection: A Survey
|
http://arxiv.org/abs/2301.11514v5
|
The recent rapid development of deep learning has laid a milestone in
industrial Image Anomaly Detection (IAD). In this paper, we provide a
comprehensive review of deep learning-based image anomaly detection techniques,
from the perspectives of neural network architectures, levels of supervision,
loss functions, metrics and datasets. In addition, we extract the new setting
from industrial manufacturing and review the current IAD approaches under our
proposed our new setting. Moreover, we highlight several opening challenges for
image anomaly detection. The merits and downsides of representative network
architectures under varying supervision are discussed. Finally, we summarize
the research findings and point out future research directions. More resources
are available at
https://github.com/M-3LAB/awesome-industrial-anomaly-detection.
| true | true |
Liu, Jiaqi and Xie, Guoyang and Wang, Jinbao and Li, Shangnian and Wang, Chengjie and Zheng, Feng and Jin, Yaochu
| 2,024 | null | null |
10.1109/cvpr52688.2022.01392
|
Machine Intelligence Research
|
Deep Industrial Image Anomaly Detection: A Survey
|
Deep Industrial Image Anomaly Detection: A Survey
|
http://arxiv.org/pdf/2301.11514v5
|
The recent rapid development of deep learning has laid a milestone in
industrial Image Anomaly Detection (IAD). In this paper, we provide a
comprehensive review of deep learning-based image anomaly detection techniques,
from the perspectives of neural network architectures, levels of supervision,
loss functions, metrics and datasets. In addition, we extract the new setting
from industrial manufacturing and review the current IAD approaches under our
proposed our new setting. Moreover, we highlight several opening challenges for
image anomaly detection. The merits and downsides of representative network
architectures under varying supervision are discussed. Finally, we summarize
the research findings and point out future research directions. More resources
are available at
https://github.com/M-3LAB/awesome-industrial-anomaly-detection.
|
KairosAD: A SAM-Based Model for Industrial Anomaly Detection on Embedded
Devices
|
2505.24334v1
|
bergmann2019mvtec
|
\cite{bergmann2019mvtec}
|
{MVTec AD — A Comprehensive Real-World Dataset for Unsupervised Anomaly Detection}
| null | null | true | false |
Bergmann, Paul and Fauser, Michael and Sattlegger, David and Steger, Carsten
| 2,019 | null | null |
10.1007/978-3-031-20056-4_23
| null |
{MVTec AD — A Comprehensive Real-World Dataset for Unsupervised Anomaly Detection}
|
The MVTec Anomaly Detection Dataset - ACM Digital Library
|
https://dl.acm.org/doi/abs/10.1007/s11263-020-01400-4
|
(2019a). MVTec AD: A comprehensive real-world dataset for unsupervised anomaly detection. In Proceedings of the IEEE conference on computer vision and pattern
|
KairosAD: A SAM-Based Model for Industrial Anomaly Detection on Embedded
Devices
|
2505.24334v1
|
bergmann2018improving
|
\cite{bergmann2018improving}
|
Improving Unsupervised Defect Segmentation by Applying Structural
Similarity to Autoencoders
|
http://arxiv.org/abs/1807.02011v3
|
Convolutional autoencoders have emerged as popular methods for unsupervised
defect segmentation on image data. Most commonly, this task is performed by
thresholding a pixel-wise reconstruction error based on an $\ell^p$ distance.
This procedure, however, leads to large residuals whenever the reconstruction
encompasses slight localization inaccuracies around edges. It also fails to
reveal defective regions that have been visually altered when intensity values
stay roughly consistent. We show that these problems prevent these approaches
from being applied to complex real-world scenarios and that it cannot be easily
avoided by employing more elaborate architectures such as variational or
feature matching autoencoders. We propose to use a perceptual loss function
based on structural similarity which examines inter-dependencies between local
image regions, taking into account luminance, contrast and structural
information, instead of simply comparing single pixel values. It achieves
significant performance gains on a challenging real-world dataset of
nanofibrous materials and a novel dataset of two woven fabrics over the state
of the art approaches for unsupervised defect segmentation that use pixel-wise
reconstruction error metrics.
| true | true |
Bergmann, Paul and Löwe, Sindy and Fauser, Michael and Sattlegger, David and Steger, Carsten
| 2,019 | null | null | null | null |
Improving Unsupervised Defect Segmentation by Applying Structural
Similarity to Autoencoders
|
(PDF) Improving Unsupervised Defect Segmentation by Applying ...
|
https://www.researchgate.net/publication/331779705_Improving_Unsupervised_Defect_Segmentation_by_Applying_Structural_Similarity_to_Autoencoders
|
Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders ; Paul Bergmann at Technical University of Munich. Paul Bergmann.
|
KairosAD: A SAM-Based Model for Industrial Anomaly Detection on Embedded
Devices
|
2505.24334v1
|
liu2020towards
|
\cite{liu2020towards}
|
Towards Visually Explaining Variational Autoencoders
|
http://arxiv.org/abs/1911.07389v7
|
Recent advances in Convolutional Neural Network (CNN) model interpretability
have led to impressive progress in visualizing and understanding model
predictions. In particular, gradient-based visual attention methods have driven
much recent effort in using visual attention maps as a means for visual
explanations. A key problem, however, is these methods are designed for
classification and categorization tasks, and their extension to explaining
generative models, e.g. variational autoencoders (VAE) is not trivial. In this
work, we take a step towards bridging this crucial gap, proposing the first
technique to visually explain VAEs by means of gradient-based attention. We
present methods to generate visual attention from the learned latent space, and
also demonstrate such attention explanations serve more than just explaining
VAE predictions. We show how these attention maps can be used to localize
anomalies in images, demonstrating state-of-the-art performance on the MVTec-AD
dataset. We also show how they can be infused into model training, helping
bootstrap the VAE into learning improved latent space disentanglement,
demonstrated on the Dsprites dataset.
| true | true |
Liu, Wenqian and Li, Runze and Zheng, Meng and Karanam, Srikrishna and Wu, Ziyan and Bhanu, Bir and Radke, Richard J. and Camps, Octavia
| 2,020 | null | null |
10.1007/978-3-030-20893-6_39
| null |
Towards Visually Explaining Variational Autoencoders
|
Towards Visually Explaining Variational Autoencoders
|
http://arxiv.org/pdf/1911.07389v7
|
Recent advances in Convolutional Neural Network (CNN) model interpretability
have led to impressive progress in visualizing and understanding model
predictions. In particular, gradient-based visual attention methods have driven
much recent effort in using visual attention maps as a means for visual
explanations. A key problem, however, is these methods are designed for
classification and categorization tasks, and their extension to explaining
generative models, e.g. variational autoencoders (VAE) is not trivial. In this
work, we take a step towards bridging this crucial gap, proposing the first
technique to visually explain VAEs by means of gradient-based attention. We
present methods to generate visual attention from the learned latent space, and
also demonstrate such attention explanations serve more than just explaining
VAE predictions. We show how these attention maps can be used to localize
anomalies in images, demonstrating state-of-the-art performance on the MVTec-AD
dataset. We also show how they can be infused into model training, helping
bootstrap the VAE into learning improved latent space disentanglement,
demonstrated on the Dsprites dataset.
|
KairosAD: A SAM-Based Model for Industrial Anomaly Detection on Embedded
Devices
|
2505.24334v1
|
akcay2019ganomaly
|
\cite{akcay2019ganomaly}
|
GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training
|
http://arxiv.org/abs/1805.06725v3
|
Anomaly detection is a classical problem in computer vision, namely the
determination of the normal from the abnormal when datasets are highly biased
towards one class (normal) due to the insufficient sample size of the other
class (abnormal). While this can be addressed as a supervised learning problem,
a significantly more challenging problem is that of detecting the
unknown/unseen anomaly case that takes us instead into the space of a
one-class, semi-supervised learning paradigm. We introduce such a novel anomaly
detection model, by using a conditional generative adversarial network that
jointly learns the generation of high-dimensional image space and the inference
of latent space. Employing encoder-decoder-encoder sub-networks in the
generator network enables the model to map the input image to a lower dimension
vector, which is then used to reconstruct the generated output image. The use
of the additional encoder network maps this generated image to its latent
representation. Minimizing the distance between these images and the latent
vectors during training aids in learning the data distribution for the normal
samples. As a result, a larger distance metric from this learned data
distribution at inference time is indicative of an outlier from that
distribution - an anomaly. Experimentation over several benchmark datasets,
from varying domains, shows the model efficacy and superiority over previous
state-of-the-art approaches.
| true | true |
Akcay, Samet and Atapour-Abarghouei, Amir and Breckon, Toby P.
| 2,019 | null | null | null | null |
GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training
|
GANomaly Paper Review: Semi-Supervised Anomaly Detection via ...
|
https://towardsdatascience.com/ganomaly-paper-review-semi-supervised-anomaly-detection-via-adversarial-training-a6f7a64a265f/
|
GANomaly is an anomaly detection model that employs adversarial training to capture the data distribution.
|
KairosAD: A SAM-Based Model for Industrial Anomaly Detection on Embedded
Devices
|
2505.24334v1
|
damm2024anomalydino
|
\cite{damm2024anomalydino}
|
AnomalyDINO: Boosting Patch-based Few-shot Anomaly Detection with DINOv2
|
http://arxiv.org/abs/2405.14529v3
|
Recent advances in multimodal foundation models have set new standards in
few-shot anomaly detection. This paper explores whether high-quality visual
features alone are sufficient to rival existing state-of-the-art
vision-language models. We affirm this by adapting DINOv2 for one-shot and
few-shot anomaly detection, with a focus on industrial applications. We show
that this approach does not only rival existing techniques but can even
outmatch them in many settings. Our proposed vision-only approach, AnomalyDINO,
follows the well-established patch-level deep nearest neighbor paradigm, and
enables both image-level anomaly prediction and pixel-level anomaly
segmentation. The approach is methodologically simple and training-free and,
thus, does not require any additional data for fine-tuning or meta-learning.
The approach is methodologically simple and training-free and, thus, does not
require any additional data for fine-tuning or meta-learning. Despite its
simplicity, AnomalyDINO achieves state-of-the-art results in one- and few-shot
anomaly detection (e.g., pushing the one-shot performance on MVTec-AD from an
AUROC of 93.1% to 96.6%). The reduced overhead, coupled with its outstanding
few-shot performance, makes AnomalyDINO a strong candidate for fast deployment,
e.g., in industrial contexts.
| true | true |
Damm, Simon and Laszkiewicz, Mike and Lederer, Johannes and Fischer, Asja
| 2,024 | null | null |
10.1561/0600000110
| null |
AnomalyDINO: Boosting Patch-based Few-shot Anomaly Detection with DINOv2
|
[PDF] Boosting Patch-Based Few-Shot Anomaly Detection with DINOv2
|
https://openaccess.thecvf.com/content/WACV2025/papers/Damm_AnomalyDINO_Boosting_Patch-Based_Few-Shot_Anomaly_Detection_with_DINOv2_WACV_2025_paper.pdf
|
Our approach, termed AnomalyDINO, follows the well- established AD framework of patch-level deep nearest neighbor [34, 46], and leverages DINOv2 [30] as a back-.
|
KairosAD: A SAM-Based Model for Industrial Anomaly Detection on Embedded
Devices
|
2505.24334v1
|
roth2022towards
|
\cite{roth2022towards}
|
Towards Total Recall in Industrial Anomaly Detection
|
http://arxiv.org/abs/2106.08265v2
|
Being able to spot defective parts is a critical component in large-scale
industrial manufacturing. A particular challenge that we address in this work
is the cold-start problem: fit a model using nominal (non-defective) example
images only. While handcrafted solutions per class are possible, the goal is to
build systems that work well simultaneously on many different tasks
automatically. The best performing approaches combine embeddings from ImageNet
models with an outlier detection model. In this paper, we extend on this line
of work and propose \textbf{PatchCore}, which uses a maximally representative
memory bank of nominal patch-features. PatchCore offers competitive inference
times while achieving state-of-the-art performance for both detection and
localization. On the challenging, widely used MVTec AD benchmark PatchCore
achieves an image-level anomaly detection AUROC score of up to $99.6\%$, more
than halving the error compared to the next best competitor. We further report
competitive results on two additional datasets and also find competitive
results in the few samples regime.\freefootnote{$^*$ Work done during a
research internship at Amazon AWS.} Code:
github.com/amazon-research/patchcore-inspection.
| true | true |
Roth, Karsten and Pemula, Latha and Zepeda, Joaquin and Scholkopf, Bernhard and Brox, Thomas and Gehler, Peter
| 2,022 | null | null |
10.1109/cvpr52688.2022.00951
| null |
Towards Total Recall in Industrial Anomaly Detection
|
Towards Total Recall in Industrial Anomaly Detection
|
http://arxiv.org/pdf/2106.08265v2
|
Being able to spot defective parts is a critical component in large-scale
industrial manufacturing. A particular challenge that we address in this work
is the cold-start problem: fit a model using nominal (non-defective) example
images only. While handcrafted solutions per class are possible, the goal is to
build systems that work well simultaneously on many different tasks
automatically. The best performing approaches combine embeddings from ImageNet
models with an outlier detection model. In this paper, we extend on this line
of work and propose \textbf{PatchCore}, which uses a maximally representative
memory bank of nominal patch-features. PatchCore offers competitive inference
times while achieving state-of-the-art performance for both detection and
localization. On the challenging, widely used MVTec AD benchmark PatchCore
achieves an image-level anomaly detection AUROC score of up to $99.6\%$, more
than halving the error compared to the next best competitor. We further report
competitive results on two additional datasets and also find competitive
results in the few samples regime.\freefootnote{$^*$ Work done during a
research internship at Amazon AWS.} Code:
github.com/amazon-research/patchcore-inspection.
|
KairosAD: A SAM-Based Model for Industrial Anomaly Detection on Embedded
Devices
|
2505.24334v1
|
jiang2022softpatch
|
\cite{jiang2022softpatch}
|
SoftPatch: Unsupervised Anomaly Detection with Noisy Data
|
http://arxiv.org/abs/2403.14233v1
|
Although mainstream unsupervised anomaly detection (AD) algorithms perform
well in academic datasets, their performance is limited in practical
application due to the ideal experimental setting of clean training data.
Training with noisy data is an inevitable problem in real-world anomaly
detection but is seldom discussed. This paper considers label-level noise in
image sensory anomaly detection for the first time. To solve this problem, we
proposed a memory-based unsupervised AD method, SoftPatch, which efficiently
denoises the data at the patch level. Noise discriminators are utilized to
generate outlier scores for patch-level noise elimination before coreset
construction. The scores are then stored in the memory bank to soften the
anomaly detection boundary. Compared with existing methods, SoftPatch maintains
a strong modeling ability of normal data and alleviates the overconfidence
problem in coreset. Comprehensive experiments in various noise scenes
demonstrate that SoftPatch outperforms the state-of-the-art AD methods on the
MVTecAD and BTAD benchmarks and is comparable to those methods under the
setting without noise.
| true | true |
Jiang, Xi and Liu, Jianlin and Wang, Jinbao and Nie, Qiang and Wu, Kai and Liu, Yong and Wang, Chengjie and Zheng, Feng
| 2,022 | null | null | null | null |
SoftPatch: Unsupervised Anomaly Detection with Noisy Data
|
SoftPatch: Unsupervised Anomaly Detection with Noisy Data
|
http://arxiv.org/pdf/2403.14233v1
|
Although mainstream unsupervised anomaly detection (AD) algorithms perform
well in academic datasets, their performance is limited in practical
application due to the ideal experimental setting of clean training data.
Training with noisy data is an inevitable problem in real-world anomaly
detection but is seldom discussed. This paper considers label-level noise in
image sensory anomaly detection for the first time. To solve this problem, we
proposed a memory-based unsupervised AD method, SoftPatch, which efficiently
denoises the data at the patch level. Noise discriminators are utilized to
generate outlier scores for patch-level noise elimination before coreset
construction. The scores are then stored in the memory bank to soften the
anomaly detection boundary. Compared with existing methods, SoftPatch maintains
a strong modeling ability of normal data and alleviates the overconfidence
problem in coreset. Comprehensive experiments in various noise scenes
demonstrate that SoftPatch outperforms the state-of-the-art AD methods on the
MVTecAD and BTAD benchmarks and is comparable to those methods under the
setting without noise.
|
KairosAD: A SAM-Based Model for Industrial Anomaly Detection on Embedded
Devices
|
2505.24334v1
|
li2024sam
|
\cite{li2024sam}
|
A SAM-guided Two-stream Lightweight Model for Anomaly Detection
|
http://arxiv.org/abs/2402.19145v2
|
In industrial anomaly detection, model efficiency and mobile-friendliness
become the primary concerns in real-world applications. Simultaneously, the
impressive generalization capabilities of Segment Anything (SAM) have garnered
broad academic attention, making it an ideal choice for localizing unseen
anomalies and diverse real-world patterns. In this paper, considering these two
critical factors, we propose a SAM-guided Two-stream Lightweight Model for
unsupervised anomaly detection (STLM) that not only aligns with the two
practical application requirements but also harnesses the robust generalization
capabilities of SAM. We employ two lightweight image encoders, i.e., our
two-stream lightweight module, guided by SAM's knowledge. To be specific, one
stream is trained to generate discriminative and general feature
representations in both normal and anomalous regions, while the other stream
reconstructs the same images without anomalies, which effectively enhances the
differentiation of two-stream representations when facing anomalous regions.
Furthermore, we employ a shared mask decoder and a feature aggregation module
to generate anomaly maps. Our experiments conducted on MVTec AD benchmark show
that STLM, with about 16M parameters and achieving an inference time in 20ms,
competes effectively with state-of-the-art methods in terms of performance,
98.26% on pixel-level AUC and 94.92% on PRO. We further experiment on more
difficult datasets, e.g., VisA and DAGM, to demonstrate the effectiveness and
generalizability of STLM.
| true | true |
Li, Chenghao and Qi, Lei and Geng, Xin
| 2,025 | null | null |
10.1109/cvpr.2019.00982
|
ACM Transactions on Multimedia Computing, Communications, and Applications
|
A SAM-guided Two-stream Lightweight Model for Anomaly Detection
|
A SAM-guided Two-stream Lightweight Model for Anomaly Detection
|
https://arxiv.org/html/2402.19145v1
|
In this paper, we propose a novel framework called SAM-guided Two-stream Lightweight Model for unsupervised anomaly detection tasks.
|
KairosAD: A SAM-Based Model for Industrial Anomaly Detection on Embedded
Devices
|
2505.24334v1
|
li2024multimodal
|
\cite{li2024multimodal}
|
Multimodal Foundation Models: From Specialists to General-Purpose
Assistants
|
http://arxiv.org/abs/2309.10020v1
|
This paper presents a comprehensive survey of the taxonomy and evolution of
multimodal foundation models that demonstrate vision and vision-language
capabilities, focusing on the transition from specialist models to
general-purpose assistants. The research landscape encompasses five core
topics, categorized into two classes. (i) We start with a survey of
well-established research areas: multimodal foundation models pre-trained for
specific purposes, including two topics -- methods of learning vision backbones
for visual understanding and text-to-image generation. (ii) Then, we present
recent advances in exploratory, open research areas: multimodal foundation
models that aim to play the role of general-purpose assistants, including three
topics -- unified vision models inspired by large language models (LLMs),
end-to-end training of multimodal LLMs, and chaining multimodal tools with
LLMs. The target audiences of the paper are researchers, graduate students, and
professionals in computer vision and vision-language multimodal communities who
are eager to learn the basics and recent advances in multimodal foundation
models.
| true | true |
Li, Chunyuan and Gan, Zhe and Yang, Zhengyuan and Yang, Jianwei and Li, Linjie and Wang, Lijuan and Gao, Jianfeng
| 2,024 | null | null | null |
Foundations and Trends in Computer Graphics and Vision
|
Multimodal Foundation Models: From Specialists to General-Purpose
Assistants
|
Multimodal Foundation Models: From Specialists to ...
|
https://www.nowpublishers.com/article/Details/CGV-110
|
by C Li · 2024 · Cited by 316 — This monograph presents a comprehensive survey of the taxonomy and evolution of multimodal foundation models that demonstrate vision and vision-language
|
KairosAD: A SAM-Based Model for Industrial Anomaly Detection on Embedded
Devices
|
2505.24334v1
|
radford2021learning
|
\cite{radford2021learning}
|
Learning Transferable Visual Models From Natural Language Supervision
|
http://arxiv.org/abs/2103.00020v1
|
State-of-the-art computer vision systems are trained to predict a fixed set
of predetermined object categories. This restricted form of supervision limits
their generality and usability since additional labeled data is needed to
specify any other visual concept. Learning directly from raw text about images
is a promising alternative which leverages a much broader source of
supervision. We demonstrate that the simple pre-training task of predicting
which caption goes with which image is an efficient and scalable way to learn
SOTA image representations from scratch on a dataset of 400 million (image,
text) pairs collected from the internet. After pre-training, natural language
is used to reference learned visual concepts (or describe new ones) enabling
zero-shot transfer of the model to downstream tasks. We study the performance
of this approach by benchmarking on over 30 different existing computer vision
datasets, spanning tasks such as OCR, action recognition in videos,
geo-localization, and many types of fine-grained object classification. The
model transfers non-trivially to most tasks and is often competitive with a
fully supervised baseline without the need for any dataset specific training.
For instance, we match the accuracy of the original ResNet-50 on ImageNet
zero-shot without needing to use any of the 1.28 million training examples it
was trained on. We release our code and pre-trained model weights at
https://github.com/OpenAI/CLIP.
| true | true |
Radford, Alec and Kim, Jong Wook and Hallacy, Chris and Ramesh, Aditya and Goh, Gabriel and Agarwal, Sandhini and Sastry, Girish and Askell, Amanda and Mishkin, Pamela and Clark, Jack and Krueger, Gretchen and Sutskever, Ilya
| 2,021 | null | null | null | null |
Learning Transferable Visual Models From Natural Language Supervision
|
Learning Transferable Visual Models From Natural Language Supervision
|
http://arxiv.org/pdf/2103.00020v1
|
State-of-the-art computer vision systems are trained to predict a fixed set
of predetermined object categories. This restricted form of supervision limits
their generality and usability since additional labeled data is needed to
specify any other visual concept. Learning directly from raw text about images
is a promising alternative which leverages a much broader source of
supervision. We demonstrate that the simple pre-training task of predicting
which caption goes with which image is an efficient and scalable way to learn
SOTA image representations from scratch on a dataset of 400 million (image,
text) pairs collected from the internet. After pre-training, natural language
is used to reference learned visual concepts (or describe new ones) enabling
zero-shot transfer of the model to downstream tasks. We study the performance
of this approach by benchmarking on over 30 different existing computer vision
datasets, spanning tasks such as OCR, action recognition in videos,
geo-localization, and many types of fine-grained object classification. The
model transfers non-trivially to most tasks and is often competitive with a
fully supervised baseline without the need for any dataset specific training.
For instance, we match the accuracy of the original ResNet-50 on ImageNet
zero-shot without needing to use any of the 1.28 million training examples it
was trained on. We release our code and pre-trained model weights at
https://github.com/OpenAI/CLIP.
|
KairosAD: A SAM-Based Model for Industrial Anomaly Detection on Embedded
Devices
|
2505.24334v1
|
kirillov2023segment
|
\cite{kirillov2023segment}
|
Segment Anything
|
http://arxiv.org/abs/2304.02643v1
|
We introduce the Segment Anything (SA) project: a new task, model, and
dataset for image segmentation. Using our efficient model in a data collection
loop, we built the largest segmentation dataset to date (by far), with over 1
billion masks on 11M licensed and privacy respecting images. The model is
designed and trained to be promptable, so it can transfer zero-shot to new
image distributions and tasks. We evaluate its capabilities on numerous tasks
and find that its zero-shot performance is impressive -- often competitive with
or even superior to prior fully supervised results. We are releasing the
Segment Anything Model (SAM) and corresponding dataset (SA-1B) of 1B masks and
11M images at https://segment-anything.com to foster research into foundation
models for computer vision.
| true | true |
Kirillov, Alexander and Mintun, Eric and Ravi, Nikhila and Mao, Hanzi and Rolland, Chloe and Gustafson, Laura and Xiao, Tete and Whitehead, Spencer and Berg, Alexander C. and Lo, Wan-Yen and Dollar, Piotr and Girshick, Ross
| 2,023 | null | null |
10.1109/tip.2023.3293772
| null |
Segment Anything
|
Segment Anything
|
http://arxiv.org/pdf/2304.02643v1
|
We introduce the Segment Anything (SA) project: a new task, model, and
dataset for image segmentation. Using our efficient model in a data collection
loop, we built the largest segmentation dataset to date (by far), with over 1
billion masks on 11M licensed and privacy respecting images. The model is
designed and trained to be promptable, so it can transfer zero-shot to new
image distributions and tasks. We evaluate its capabilities on numerous tasks
and find that its zero-shot performance is impressive -- often competitive with
or even superior to prior fully supervised results. We are releasing the
Segment Anything Model (SAM) and corresponding dataset (SA-1B) of 1B masks and
11M images at https://segment-anything.com to foster research into foundation
models for computer vision.
|
KairosAD: A SAM-Based Model for Industrial Anomaly Detection on Embedded
Devices
|
2505.24334v1
|
caron2021emerging
|
\cite{caron2021emerging}
|
Emerging Properties in Self-Supervised Vision Transformers
|
http://arxiv.org/abs/2104.14294v2
|
In this paper, we question if self-supervised learning provides new
properties to Vision Transformer (ViT) that stand out compared to convolutional
networks (convnets). Beyond the fact that adapting self-supervised methods to
this architecture works particularly well, we make the following observations:
first, self-supervised ViT features contain explicit information about the
semantic segmentation of an image, which does not emerge as clearly with
supervised ViTs, nor with convnets. Second, these features are also excellent
k-NN classifiers, reaching 78.3% top-1 on ImageNet with a small ViT. Our study
also underlines the importance of momentum encoder, multi-crop training, and
the use of small patches with ViTs. We implement our findings into a simple
self-supervised method, called DINO, which we interpret as a form of
self-distillation with no labels. We show the synergy between DINO and ViTs by
achieving 80.1% top-1 on ImageNet in linear evaluation with ViT-Base.
| true | true |
Caron, Mathilde and Touvron, Hugo and Misra, Ishan and J\'egou, Herv\'e and Mairal, Julien and Bojanowski, Piotr and Joulin, Armand
| 2,021 | null | null | null | null |
Emerging Properties in Self-Supervised Vision Transformers
|
[PDF] Emerging Properties in Self-Supervised Vision Transformers
|
https://openaccess.thecvf.com/content/ICCV2021/papers/Caron_Emerging_Properties_in_Self-Supervised_Vision_Transformers_ICCV_2021_paper.pdf
|
Self-supervised ViT features contain semantic segmentation, scene layout, object boundaries, and perform well with k-NN classifiers, unlike supervised ViTs or
|
KairosAD: A SAM-Based Model for Industrial Anomaly Detection on Embedded
Devices
|
2505.24334v1
|
oquab2023dinov2
|
\cite{oquab2023dinov2}
|
DINOv2: Learning Robust Visual Features without Supervision
|
http://arxiv.org/abs/2304.07193v2
|
The recent breakthroughs in natural language processing for model pretraining
on large quantities of data have opened the way for similar foundation models
in computer vision. These models could greatly simplify the use of images in
any system by producing all-purpose visual features, i.e., features that work
across image distributions and tasks without finetuning. This work shows that
existing pretraining methods, especially self-supervised methods, can produce
such features if trained on enough curated data from diverse sources. We
revisit existing approaches and combine different techniques to scale our
pretraining in terms of data and model size. Most of the technical
contributions aim at accelerating and stabilizing the training at scale. In
terms of data, we propose an automatic pipeline to build a dedicated, diverse,
and curated image dataset instead of uncurated data, as typically done in the
self-supervised literature. In terms of models, we train a ViT model
(Dosovitskiy et al., 2020) with 1B parameters and distill it into a series of
smaller models that surpass the best available all-purpose features, OpenCLIP
(Ilharco et al., 2021) on most of the benchmarks at image and pixel levels.
| true | true |
Maxime Oquab and Timoth{\'e}e Darcet and Th{\'e}o Moutakanni and Huy V. Vo and Marc Szafraniec and Vasil Khalidov and Pierre Fernandez and Daniel HAZIZA and Francisco Massa and Alaaeldin El-Nouby and Mido Assran and Nicolas Ballas and Wojciech Galuba and Russell Howes and Po-Yao Huang and Shang-Wen Li and Ishan Misra and Michael Rabbat and Vasu Sharma and Gabriel Synnaeve and Hu Xu and Herve Jegou and Julien Mairal and Patrick Labatut and Armand Joulin and Piotr Bojanowski
| 2,024 | null | null | null |
Transactions on Machine Learning Research
|
DINOv2: Learning Robust Visual Features without Supervision
|
DINOv2: Learning Robust Visual Features without Supervision
|
http://arxiv.org/pdf/2304.07193v2
|
The recent breakthroughs in natural language processing for model pretraining
on large quantities of data have opened the way for similar foundation models
in computer vision. These models could greatly simplify the use of images in
any system by producing all-purpose visual features, i.e., features that work
across image distributions and tasks without finetuning. This work shows that
existing pretraining methods, especially self-supervised methods, can produce
such features if trained on enough curated data from diverse sources. We
revisit existing approaches and combine different techniques to scale our
pretraining in terms of data and model size. Most of the technical
contributions aim at accelerating and stabilizing the training at scale. In
terms of data, we propose an automatic pipeline to build a dedicated, diverse,
and curated image dataset instead of uncurated data, as typically done in the
self-supervised literature. In terms of models, we train a ViT model
(Dosovitskiy et al., 2020) with 1B parameters and distill it into a series of
smaller models that surpass the best available all-purpose features, OpenCLIP
(Ilharco et al., 2021) on most of the benchmarks at image and pixel levels.
|
KairosAD: A SAM-Based Model for Industrial Anomaly Detection on Embedded
Devices
|
2505.24334v1
|
zhang2023faster
|
\cite{zhang2023faster}
|
Faster Segment Anything: Towards Lightweight SAM for Mobile Applications
|
http://arxiv.org/abs/2306.14289v2
|
Segment Anything Model (SAM) has attracted significant attention due to its
impressive zero-shot transfer performance and high versatility for numerous
vision applications (like image editing with fine-grained control). Many of
such applications need to be run on resource-constraint edge devices, like
mobile phones. In this work, we aim to make SAM mobile-friendly by replacing
the heavyweight image encoder with a lightweight one. A naive way to train such
a new SAM as in the original SAM paper leads to unsatisfactory performance,
especially when limited training sources are available. We find that this is
mainly caused by the coupled optimization of the image encoder and mask
decoder, motivated by which we propose decoupled distillation. Concretely, we
distill the knowledge from the heavy image encoder (ViT-H in the original SAM)
to a lightweight image encoder, which can be automatically compatible with the
mask decoder in the original SAM. The training can be completed on a single GPU
within less than one day, and the resulting lightweight SAM is termed MobileSAM
which is more than 60 times smaller yet performs on par with the original SAM.
For inference speed, With a single GPU, MobileSAM runs around 10ms per image:
8ms on the image encoder and 4ms on the mask decoder. With superior
performance, our MobileSAM is around 5 times faster than the concurrent FastSAM
and 7 times smaller, making it more suitable for mobile applications. Moreover,
we show that MobileSAM can run relatively smoothly on CPU. The code for our
project is provided at
\href{https://github.com/ChaoningZhang/MobileSAM}{\textcolor{red}{MobileSAM}}),
with a demo showing that MobileSAM can run relatively smoothly on CPU.
| true | true |
Zhang, Chaoning and Han, Dongshen and Qiao, Yu and Kim, Jung Uk and Bae, Sung-Ho and Lee, Seungkyu and Hong, Choong Seon
| 2,023 | null | null |
10.1109/iccv48922.2021.00822
|
arXiv preprint arXiv:2306.14289
|
Faster Segment Anything: Towards Lightweight SAM for Mobile Applications
|
Faster Segment Anything: Towards Lightweight SAM for Mobile Applications
|
http://arxiv.org/pdf/2306.14289v2
|
Segment Anything Model (SAM) has attracted significant attention due to its
impressive zero-shot transfer performance and high versatility for numerous
vision applications (like image editing with fine-grained control). Many of
such applications need to be run on resource-constraint edge devices, like
mobile phones. In this work, we aim to make SAM mobile-friendly by replacing
the heavyweight image encoder with a lightweight one. A naive way to train such
a new SAM as in the original SAM paper leads to unsatisfactory performance,
especially when limited training sources are available. We find that this is
mainly caused by the coupled optimization of the image encoder and mask
decoder, motivated by which we propose decoupled distillation. Concretely, we
distill the knowledge from the heavy image encoder (ViT-H in the original SAM)
to a lightweight image encoder, which can be automatically compatible with the
mask decoder in the original SAM. The training can be completed on a single GPU
within less than one day, and the resulting lightweight SAM is termed MobileSAM
which is more than 60 times smaller yet performs on par with the original SAM.
For inference speed, With a single GPU, MobileSAM runs around 10ms per image:
8ms on the image encoder and 4ms on the mask decoder. With superior
performance, our MobileSAM is around 5 times faster than the concurrent FastSAM
and 7 times smaller, making it more suitable for mobile applications. Moreover,
we show that MobileSAM can run relatively smoothly on CPU. The code for our
project is provided at
\href{https://github.com/ChaoningZhang/MobileSAM}{\textcolor{red}{MobileSAM}}),
with a demo showing that MobileSAM can run relatively smoothly on CPU.
|
KairosAD: A SAM-Based Model for Industrial Anomaly Detection on Embedded
Devices
|
2505.24334v1
|
capogrosso2024machine
|
\cite{capogrosso2024machine}
|
A Machine Learning-oriented Survey on Tiny Machine Learning
|
http://arxiv.org/abs/2309.11932v2
|
The emergence of Tiny Machine Learning (TinyML) has positively revolutionized
the field of Artificial Intelligence by promoting the joint design of
resource-constrained IoT hardware devices and their learning-based software
architectures. TinyML carries an essential role within the fourth and fifth
industrial revolutions in helping societies, economies, and individuals employ
effective AI-infused computing technologies (e.g., smart cities, automotive,
and medical robotics). Given its multidisciplinary nature, the field of TinyML
has been approached from many different angles: this comprehensive survey
wishes to provide an up-to-date overview focused on all the learning algorithms
within TinyML-based solutions. The survey is based on the Preferred Reporting
Items for Systematic Reviews and Meta-Analyses (PRISMA) methodological flow,
allowing for a systematic and complete literature survey. In particular,
firstly we will examine the three different workflows for implementing a
TinyML-based system, i.e., ML-oriented, HW-oriented, and co-design. Secondly,
we propose a taxonomy that covers the learning panorama under the TinyML lens,
examining in detail the different families of model optimization and design, as
well as the state-of-the-art learning techniques. Thirdly, this survey will
present the distinct features of hardware devices and software tools that
represent the current state-of-the-art for TinyML intelligent edge
applications. Finally, we discuss the challenges and future directions.
| true | true |
Capogrosso, Luigi and Cunico, Federico and Cheng, Dong Seon and Fummi, Franco and Cristani, Marco
| 2,024 | null | null |
10.1109/access.2022.3182659
|
IEEE Access
|
A Machine Learning-oriented Survey on Tiny Machine Learning
|
(PDF) A Machine Learning-Oriented Survey on Tiny Machine Learning
|
https://www.researchgate.net/publication/378163073_A_Machine_Learning-oriented_Survey_on_Tiny_Machine_Learning
|
This comprehensive survey wishes to provide an up-to-date overview focused on all the learning algorithms within TinyML-based solutions.
|
KairosAD: A SAM-Based Model for Industrial Anomaly Detection on Embedded
Devices
|
2505.24334v1
|
vadera2022methods
|
\cite{vadera2022methods}
|
Methods for Pruning Deep Neural Networks
|
http://arxiv.org/abs/2011.00241v2
|
This paper presents a survey of methods for pruning deep neural networks. It
begins by categorising over 150 studies based on the underlying approach used
and then focuses on three categories: methods that use magnitude based pruning,
methods that utilise clustering to identify redundancy, and methods that use
sensitivity analysis to assess the effect of pruning. Some of the key
influencing studies within these categories are presented to highlight the
underlying approaches and results achieved. Most studies present results which
are distributed in the literature as new architectures, algorithms and data
sets have developed with time, making comparison across different studied
difficult. The paper therefore provides a resource for the community that can
be used to quickly compare the results from many different methods on a variety
of data sets, and a range of architectures, including AlexNet, ResNet, DenseNet
and VGG. The resource is illustrated by comparing the results published for
pruning AlexNet and ResNet50 on ImageNet and ResNet56 and VGG16 on the CIFAR10
data to reveal which pruning methods work well in terms of retaining accuracy
whilst achieving good compression rates. The paper concludes by identifying
some promising directions for future research.
| true | true |
Vadera, Sunil and Ameen, Salem
| 2,022 | null | null |
10.1201/9781003162810-13
|
IEEE Access
|
Methods for Pruning Deep Neural Networks
|
Methods for Pruning Deep Neural Networks
|
http://arxiv.org/pdf/2011.00241v2
|
This paper presents a survey of methods for pruning deep neural networks. It
begins by categorising over 150 studies based on the underlying approach used
and then focuses on three categories: methods that use magnitude based pruning,
methods that utilise clustering to identify redundancy, and methods that use
sensitivity analysis to assess the effect of pruning. Some of the key
influencing studies within these categories are presented to highlight the
underlying approaches and results achieved. Most studies present results which
are distributed in the literature as new architectures, algorithms and data
sets have developed with time, making comparison across different studied
difficult. The paper therefore provides a resource for the community that can
be used to quickly compare the results from many different methods on a variety
of data sets, and a range of architectures, including AlexNet, ResNet, DenseNet
and VGG. The resource is illustrated by comparing the results published for
pruning AlexNet and ResNet50 on ImageNet and ResNet56 and VGG16 on the CIFAR10
data to reveal which pruning methods work well in terms of retaining accuracy
whilst achieving good compression rates. The paper concludes by identifying
some promising directions for future research.
|
KairosAD: A SAM-Based Model for Industrial Anomaly Detection on Embedded
Devices
|
2505.24334v1
|
gholami2022survey
|
\cite{gholami2022survey}
|
A Survey of Quantization Methods for Efficient Neural Network Inference
|
http://arxiv.org/abs/2103.13630v3
|
As soon as abstract mathematical computations were adapted to computation on
digital computers, the problem of efficient representation, manipulation, and
communication of the numerical values in those computations arose. Strongly
related to the problem of numerical representation is the problem of
quantization: in what manner should a set of continuous real-valued numbers be
distributed over a fixed discrete set of numbers to minimize the number of bits
required and also to maximize the accuracy of the attendant computations? This
perennial problem of quantization is particularly relevant whenever memory
and/or computational resources are severely restricted, and it has come to the
forefront in recent years due to the remarkable performance of Neural Network
models in computer vision, natural language processing, and related areas.
Moving from floating-point representations to low-precision fixed integer
values represented in four bits or less holds the potential to reduce the
memory footprint and latency by a factor of 16x; and, in fact, reductions of 4x
to 8x are often realized in practice in these applications. Thus, it is not
surprising that quantization has emerged recently as an important and very
active sub-area of research in the efficient implementation of computations
associated with Neural Networks. In this article, we survey approaches to the
problem of quantizing the numerical values in deep Neural Network computations,
covering the advantages/disadvantages of current methods. With this survey and
its organization, we hope to have presented a useful snapshot of the current
research in quantization for Neural Networks and to have given an intelligent
organization to ease the evaluation of future research in this area.
| true | true |
Gholami, Amir and Kim, Sehoon and Dong, Zhen and Yao, Zhewei and Mahoney, Michael W. and Keutzer, Kurt
| 2,022 | null | null |
10.1007/s11263-021-01453-z
| null |
A Survey of Quantization Methods for Efficient Neural Network Inference
|
A Survey of Quantization Methods for Efficient Neural Network Inference
|
http://arxiv.org/pdf/2103.13630v3
|
As soon as abstract mathematical computations were adapted to computation on
digital computers, the problem of efficient representation, manipulation, and
communication of the numerical values in those computations arose. Strongly
related to the problem of numerical representation is the problem of
quantization: in what manner should a set of continuous real-valued numbers be
distributed over a fixed discrete set of numbers to minimize the number of bits
required and also to maximize the accuracy of the attendant computations? This
perennial problem of quantization is particularly relevant whenever memory
and/or computational resources are severely restricted, and it has come to the
forefront in recent years due to the remarkable performance of Neural Network
models in computer vision, natural language processing, and related areas.
Moving from floating-point representations to low-precision fixed integer
values represented in four bits or less holds the potential to reduce the
memory footprint and latency by a factor of 16x; and, in fact, reductions of 4x
to 8x are often realized in practice in these applications. Thus, it is not
surprising that quantization has emerged recently as an important and very
active sub-area of research in the efficient implementation of computations
associated with Neural Networks. In this article, we survey approaches to the
problem of quantizing the numerical values in deep Neural Network computations,
covering the advantages/disadvantages of current methods. With this survey and
its organization, we hope to have presented a useful snapshot of the current
research in quantization for Neural Networks and to have given an intelligent
organization to ease the evaluation of future research in this area.
|
KairosAD: A SAM-Based Model for Industrial Anomaly Detection on Embedded
Devices
|
2505.24334v1
|
gou2021knowledge
|
\cite{gou2021knowledge}
|
Knowledge Distillation: A Survey
|
http://arxiv.org/abs/2006.05525v7
|
In recent years, deep neural networks have been successful in both industry
and academia, especially for computer vision tasks. The great success of deep
learning is mainly due to its scalability to encode large-scale data and to
maneuver billions of model parameters. However, it is a challenge to deploy
these cumbersome deep models on devices with limited resources, e.g., mobile
phones and embedded devices, not only because of the high computational
complexity but also the large storage requirements. To this end, a variety of
model compression and acceleration techniques have been developed. As a
representative type of model compression and acceleration, knowledge
distillation effectively learns a small student model from a large teacher
model. It has received rapid increasing attention from the community. This
paper provides a comprehensive survey of knowledge distillation from the
perspectives of knowledge categories, training schemes, teacher-student
architecture, distillation algorithms, performance comparison and applications.
Furthermore, challenges in knowledge distillation are briefly reviewed and
comments on future research are discussed and forwarded.
| true | true |
Gou, Jianping and Yu, Baosheng and Maybank, Stephen J. and Tao, Dacheng
| 2,021 | null | null | null |
International Journal of Computer Vision
|
Knowledge Distillation: A Survey
|
Knowledge Distillation: A Survey
|
http://arxiv.org/pdf/2006.05525v7
|
In recent years, deep neural networks have been successful in both industry
and academia, especially for computer vision tasks. The great success of deep
learning is mainly due to its scalability to encode large-scale data and to
maneuver billions of model parameters. However, it is a challenge to deploy
these cumbersome deep models on devices with limited resources, e.g., mobile
phones and embedded devices, not only because of the high computational
complexity but also the large storage requirements. To this end, a variety of
model compression and acceleration techniques have been developed. As a
representative type of model compression and acceleration, knowledge
distillation effectively learns a small student model from a large teacher
model. It has received rapid increasing attention from the community. This
paper provides a comprehensive survey of knowledge distillation from the
perspectives of knowledge categories, training schemes, teacher-student
architecture, distillation algorithms, performance comparison and applications.
Furthermore, challenges in knowledge distillation are briefly reviewed and
comments on future research are discussed and forwarded.
|
KairosAD: A SAM-Based Model for Industrial Anomaly Detection on Embedded
Devices
|
2505.24334v1
|
ren2021comprehensive
|
\cite{ren2021comprehensive}
|
A Comprehensive Survey of Neural Architecture Search: Challenges and
Solutions
|
http://arxiv.org/abs/2006.02903v3
|
Deep learning has made breakthroughs and substantial in many fields due to
its powerful automatic representation capabilities. It has been proven that
neural architecture design is crucial to the feature representation of data and
the final performance. However, the design of the neural architecture heavily
relies on the researchers' prior knowledge and experience. And due to the
limitations of human' inherent knowledge, it is difficult for people to jump
out of their original thinking paradigm and design an optimal model. Therefore,
an intuitive idea would be to reduce human intervention as much as possible and
let the algorithm automatically design the neural architecture. Neural
Architecture Search (NAS) is just such a revolutionary algorithm, and the
related research work is complicated and rich. Therefore, a comprehensive and
systematic survey on the NAS is essential. Previously related surveys have
begun to classify existing work mainly based on the key components of NAS:
search space, search strategy, and evaluation strategy. While this
classification method is more intuitive, it is difficult for readers to grasp
the challenges and the landmark work involved. Therefore, in this survey, we
provide a new perspective: beginning with an overview of the characteristics of
the earliest NAS algorithms, summarizing the problems in these early NAS
algorithms, and then providing solutions for subsequent related research work.
Besides, we conduct a detailed and comprehensive analysis, comparison, and
summary of these works. Finally, we provide some possible future research
directions.
| true | true |
Ren, Pengzhen and Xiao, Yun and Chang, Xiaojun and Huang, Po-yao and Li, Zhihui and Chen, Xiaojiang and Wang, Xin
| 2,021 | null | null |
10.1109/tkde.2021.3126456
|
ACM Computing Surveys
|
A Comprehensive Survey of Neural Architecture Search: Challenges and
Solutions
|
A quick look at NAS (Neural Architecture Search) - Welcome
|
https://gachiemchiep.github.io/machine%20learning/NAS-survey-2020/
|
On this page. 2020 NAS surveyr A Comprehensive Survey of Neural Architecture Search: Challenges and Solutions. The current research results
|
KairosAD: A SAM-Based Model for Industrial Anomaly Detection on Embedded
Devices
|
2505.24334v1
|
brauwers2021general
|
\cite{brauwers2021general}
|
A General Survey on Attention Mechanisms in Deep Learning
|
http://arxiv.org/abs/2203.14263v1
|
Attention is an important mechanism that can be employed for a variety of
deep learning models across many different domains and tasks. This survey
provides an overview of the most important attention mechanisms proposed in the
literature. The various attention mechanisms are explained by means of a
framework consisting of a general attention model, uniform notation, and a
comprehensive taxonomy of attention mechanisms. Furthermore, the various
measures for evaluating attention models are reviewed, and methods to
characterize the structure of attention models based on the proposed framework
are discussed. Last, future work in the field of attention models is
considered.
| true | true |
Brauwers, Gianni and Frasincar, Flavius
| 2,023 | null | null | null |
IEEE Transactions on Knowledge and Data Engineering
|
A General Survey on Attention Mechanisms in Deep Learning
|
A General Survey on Attention Mechanisms in Deep Learning
|
http://arxiv.org/pdf/2203.14263v1
|
Attention is an important mechanism that can be employed for a variety of
deep learning models across many different domains and tasks. This survey
provides an overview of the most important attention mechanisms proposed in the
literature. The various attention mechanisms are explained by means of a
framework consisting of a general attention model, uniform notation, and a
comprehensive taxonomy of attention mechanisms. Furthermore, the various
measures for evaluating attention models are reviewed, and methods to
characterize the structure of attention models based on the proposed framework
are discussed. Last, future work in the field of attention models is
considered.
|
KairosAD: A SAM-Based Model for Industrial Anomaly Detection on Embedded
Devices
|
2505.24334v1
|
vaswani2017attention
|
\cite{vaswani2017attention}
|
Attention Is All You Need
|
http://arxiv.org/abs/1706.03762v7
|
The dominant sequence transduction models are based on complex recurrent or
convolutional neural networks in an encoder-decoder configuration. The best
performing models also connect the encoder and decoder through an attention
mechanism. We propose a new simple network architecture, the Transformer, based
solely on attention mechanisms, dispensing with recurrence and convolutions
entirely. Experiments on two machine translation tasks show these models to be
superior in quality while being more parallelizable and requiring significantly
less time to train. Our model achieves 28.4 BLEU on the WMT 2014
English-to-German translation task, improving over the existing best results,
including ensembles by over 2 BLEU. On the WMT 2014 English-to-French
translation task, our model establishes a new single-model state-of-the-art
BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction
of the training costs of the best models from the literature. We show that the
Transformer generalizes well to other tasks by applying it successfully to
English constituency parsing both with large and limited training data.
| true | true |
Vaswani, Ashish and Shazeer, Noam and Parmar, Niki and Uszkoreit, Jakob and Jones, Llion and Gomez, Aidan N and Kaiser, {\L}ukasz and Polosukhin, Illia
| 2,017 | null | null |
10.1145/3505244
| null |
Attention Is All You Need
|
Attention Is All You Need
|
http://arxiv.org/pdf/1706.03762v7
|
The dominant sequence transduction models are based on complex recurrent or
convolutional neural networks in an encoder-decoder configuration. The best
performing models also connect the encoder and decoder through an attention
mechanism. We propose a new simple network architecture, the Transformer, based
solely on attention mechanisms, dispensing with recurrence and convolutions
entirely. Experiments on two machine translation tasks show these models to be
superior in quality while being more parallelizable and requiring significantly
less time to train. Our model achieves 28.4 BLEU on the WMT 2014
English-to-German translation task, improving over the existing best results,
including ensembles by over 2 BLEU. On the WMT 2014 English-to-French
translation task, our model establishes a new single-model state-of-the-art
BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction
of the training costs of the best models from the literature. We show that the
Transformer generalizes well to other tasks by applying it successfully to
English constituency parsing both with large and limited training data.
|
KairosAD: A SAM-Based Model for Industrial Anomaly Detection on Embedded
Devices
|
2505.24334v1
|
khan2022transformers
|
\cite{khan2022transformers}
|
Transformers in Vision: A Survey
|
http://arxiv.org/abs/2101.01169v5
|
Astounding results from Transformer models on natural language tasks have
intrigued the vision community to study their application to computer vision
problems. Among their salient benefits, Transformers enable modeling long
dependencies between input sequence elements and support parallel processing of
sequence as compared to recurrent networks e.g., Long short-term memory (LSTM).
Different from convolutional networks, Transformers require minimal inductive
biases for their design and are naturally suited as set-functions. Furthermore,
the straightforward design of Transformers allows processing multiple
modalities (e.g., images, videos, text and speech) using similar processing
blocks and demonstrates excellent scalability to very large capacity networks
and huge datasets. These strengths have led to exciting progress on a number of
vision tasks using Transformer networks. This survey aims to provide a
comprehensive overview of the Transformer models in the computer vision
discipline. We start with an introduction to fundamental concepts behind the
success of Transformers i.e., self-attention, large-scale pre-training, and
bidirectional encoding. We then cover extensive applications of transformers in
vision including popular recognition tasks (e.g., image classification, object
detection, action recognition, and segmentation), generative modeling,
multi-modal tasks (e.g., visual-question answering, visual reasoning, and
visual grounding), video processing (e.g., activity recognition, video
forecasting), low-level vision (e.g., image super-resolution, image
enhancement, and colorization) and 3D analysis (e.g., point cloud
classification and segmentation). We compare the respective advantages and
limitations of popular techniques both in terms of architectural design and
their experimental value. Finally, we provide an analysis on open research
directions and possible future works.
| true | true |
Khan, Salman and Naseer, Muzammal and Hayat, Munawar and Zamir, Syed Waqas and Khan, Fahad Shahbaz and Shah, Mubarak
| 2,022 | null | null |
10.1007/978-3-031-73209-6_1
|
ACM Computing Surveys
|
Transformers in Vision: A Survey
|
Transformers in Vision: A Survey
|
http://arxiv.org/pdf/2101.01169v5
|
Astounding results from Transformer models on natural language tasks have
intrigued the vision community to study their application to computer vision
problems. Among their salient benefits, Transformers enable modeling long
dependencies between input sequence elements and support parallel processing of
sequence as compared to recurrent networks e.g., Long short-term memory (LSTM).
Different from convolutional networks, Transformers require minimal inductive
biases for their design and are naturally suited as set-functions. Furthermore,
the straightforward design of Transformers allows processing multiple
modalities (e.g., images, videos, text and speech) using similar processing
blocks and demonstrates excellent scalability to very large capacity networks
and huge datasets. These strengths have led to exciting progress on a number of
vision tasks using Transformer networks. This survey aims to provide a
comprehensive overview of the Transformer models in the computer vision
discipline. We start with an introduction to fundamental concepts behind the
success of Transformers i.e., self-attention, large-scale pre-training, and
bidirectional encoding. We then cover extensive applications of transformers in
vision including popular recognition tasks (e.g., image classification, object
detection, action recognition, and segmentation), generative modeling,
multi-modal tasks (e.g., visual-question answering, visual reasoning, and
visual grounding), video processing (e.g., activity recognition, video
forecasting), low-level vision (e.g., image super-resolution, image
enhancement, and colorization) and 3D analysis (e.g., point cloud
classification and segmentation). We compare the respective advantages and
limitations of popular techniques both in terms of architectural design and
their experimental value. Finally, we provide an analysis on open research
directions and possible future works.
|
Wav2Sem: Plug-and-Play Audio Semantic Decoupling for 3D Speech-Driven
Facial Animation
|
2505.23290v1
|
TaylorKYMKRHM17
|
\cite{TaylorKYMKRHM17}
|
A deep learning approach for generalized speech animation
| null | null | true | false |
Sarah L. Taylor and
Taehwan Kim and
Yisong Yue and
Moshe Mahler and
James Krahe and
Anastasio Garcia Rodriguez and
Jessica K. Hodgins and
Iain A. Matthews
| 2,017 | null | null | null |
TOG
|
A deep learning approach for generalized speech animation
|
[PDF] A Deep Learning Approach for Generalized Speech Animation - TTIC
|
https://home.ttic.edu/~taehwan/taylor_etal_siggraph2017.pdf
|
We introduce a simple and efective deep learning approach to automatically generate natural looking speech animation that synchronizes to input speech.
|
Wav2Sem: Plug-and-Play Audio Semantic Decoupling for 3D Speech-Driven
Facial Animation
|
2505.23290v1
|
cao2005expressive
|
\cite{cao2005expressive}
|
Expressive Speech-driven Facial Animation with controllable emotions
|
http://arxiv.org/abs/2301.02008v2
|
It is in high demand to generate facial animation with high realism, but it
remains a challenging task. Existing approaches of speech-driven facial
animation can produce satisfactory mouth movement and lip synchronization, but
show weakness in dramatic emotional expressions and flexibility in emotion
control. This paper presents a novel deep learning-based approach for
expressive facial animation generation from speech that can exhibit
wide-spectrum facial expressions with controllable emotion type and intensity.
We propose an emotion controller module to learn the relationship between the
emotion variations (e.g., types and intensity) and the corresponding facial
expression parameters. It enables emotion-controllable facial animation, where
the target expression can be continuously adjusted as desired. The qualitative
and quantitative evaluations show that the animation generated by our method is
rich in facial emotional expressiveness while retaining accurate lip movement,
outperforming other state-of-the-art methods.
| true | true |
Cao, Yong and Tien, Wen C and Faloutsos, Petros and Pighin, Fr{\'e}d{\'e}ric
| 2,005 | null | null | null |
ACM TOG
|
Expressive Speech-driven Facial Animation with controllable emotions
|
Expressive Speech-driven Facial Animation with ...
|
https://github.com/on1262/facialanimation
|
EXPRESSIVE SPEECH-DRIVEN FACIAL ANIMATION WITH CONTROLLABLE EMOTIONS. Source code for: Expressive Speech-driven Facial Animation with controllable emotions.
|
Wav2Sem: Plug-and-Play Audio Semantic Decoupling for 3D Speech-Driven
Facial Animation
|
2505.23290v1
|
FaceFormer
|
\cite{FaceFormer}
|
FaceFormer: Speech-Driven 3D Facial Animation with Transformers
|
http://arxiv.org/abs/2112.05329v4
|
Speech-driven 3D facial animation is challenging due to the complex geometry
of human faces and the limited availability of 3D audio-visual data. Prior
works typically focus on learning phoneme-level features of short audio windows
with limited context, occasionally resulting in inaccurate lip movements. To
tackle this limitation, we propose a Transformer-based autoregressive model,
FaceFormer, which encodes the long-term audio context and autoregressively
predicts a sequence of animated 3D face meshes. To cope with the data scarcity
issue, we integrate the self-supervised pre-trained speech representations.
Also, we devise two biased attention mechanisms well suited to this specific
task, including the biased cross-modal multi-head (MH) attention and the biased
causal MH self-attention with a periodic positional encoding strategy. The
former effectively aligns the audio-motion modalities, whereas the latter
offers abilities to generalize to longer audio sequences. Extensive experiments
and a perceptual user study show that our approach outperforms the existing
state-of-the-arts. The code will be made available.
| true | true |
Yingruo Fan and
Zhaojiang Lin and
Jun Saito and
Wenping Wang and
Taku Komura
| 2,022 | null | null | null | null |
FaceFormer: Speech-Driven 3D Facial Animation with Transformers
|
[PDF] FaceFormer: Speech-Driven 3D Facial Animation With Transformers
|
https://openaccess.thecvf.com/content/CVPR2022/papers/Fan_FaceFormer_Speech-Driven_3D_Facial_Animation_With_Transformers_CVPR_2022_paper.pdf
|
An autoregressive transformer-based architecture for speech-driven 3D facial animation. FaceFormer encodes the long-term audio context and the history of face
|
Wav2Sem: Plug-and-Play Audio Semantic Decoupling for 3D Speech-Driven
Facial Animation
|
2505.23290v1
|
CodeTalker
|
\cite{CodeTalker}
|
CodeTalker: Speech-Driven 3D Facial Animation with Discrete Motion Prior
|
http://arxiv.org/abs/2301.02379v2
|
Speech-driven 3D facial animation has been widely studied, yet there is still
a gap to achieving realism and vividness due to the highly ill-posed nature and
scarcity of audio-visual data. Existing works typically formulate the
cross-modal mapping into a regression task, which suffers from the
regression-to-mean problem leading to over-smoothed facial motions. In this
paper, we propose to cast speech-driven facial animation as a code query task
in a finite proxy space of the learned codebook, which effectively promotes the
vividness of the generated motions by reducing the cross-modal mapping
uncertainty. The codebook is learned by self-reconstruction over real facial
motions and thus embedded with realistic facial motion priors. Over the
discrete motion space, a temporal autoregressive model is employed to
sequentially synthesize facial motions from the input speech signal, which
guarantees lip-sync as well as plausible facial expressions. We demonstrate
that our approach outperforms current state-of-the-art methods both
qualitatively and quantitatively. Also, a user study further justifies our
superiority in perceptual quality.
| true | true |
Jinbo Xing and
Menghan Xia and
Yuechen Zhang and
Xiaodong Cun and
Jue Wang and
Tien{-}Tsin Wong
| 2,023 | null | null | null | null |
CodeTalker: Speech-Driven 3D Facial Animation with Discrete Motion Prior
|
Speech-Driven 3D Facial Animation with Discrete Motion Prior - arXiv
|
https://arxiv.org/abs/2301.02379
|
In this paper, we propose to cast speech-driven facial animation as a code query task in a finite proxy space of the learned codebook.
|
Wav2Sem: Plug-and-Play Audio Semantic Decoupling for 3D Speech-Driven
Facial Animation
|
2505.23290v1
|
FaceDiffuser
|
\cite{FaceDiffuser}
|
FaceDiffuser: Speech-Driven 3D Facial Animation Synthesis Using
Diffusion
|
http://arxiv.org/abs/2309.11306v1
|
Speech-driven 3D facial animation synthesis has been a challenging task both
in industry and research. Recent methods mostly focus on deterministic deep
learning methods meaning that given a speech input, the output is always the
same. However, in reality, the non-verbal facial cues that reside throughout
the face are non-deterministic in nature. In addition, majority of the
approaches focus on 3D vertex based datasets and methods that are compatible
with existing facial animation pipelines with rigged characters is scarce. To
eliminate these issues, we present FaceDiffuser, a non-deterministic deep
learning model to generate speech-driven facial animations that is trained with
both 3D vertex and blendshape based datasets. Our method is based on the
diffusion technique and uses the pre-trained large speech representation model
HuBERT to encode the audio input. To the best of our knowledge, we are the
first to employ the diffusion method for the task of speech-driven 3D facial
animation synthesis. We have run extensive objective and subjective analyses
and show that our approach achieves better or comparable results in comparison
to the state-of-the-art methods. We also introduce a new in-house dataset that
is based on a blendshape based rigged character. We recommend watching the
accompanying supplementary video. The code and the dataset will be publicly
available.
| true | true |
Stefan Stan and
Kazi Injamamul Haque and
Zerrin Yumak
| 2,023 | null | null | null | null |
FaceDiffuser: Speech-Driven 3D Facial Animation Synthesis Using
Diffusion
|
Speech-Driven 3D Facial Animation Synthesis Using Diffusion
|
https://dl.acm.org/doi/10.1145/3623264.3624447
|
We present FaceDiffuser, a non-deterministic deep learning model to generate speech-driven facial animations that is trained with both 3D vertex and blendshape
|
Wav2Sem: Plug-and-Play Audio Semantic Decoupling for 3D Speech-Driven
Facial Animation
|
2505.23290v1
|
li2023mask
|
\cite{li2023mask}
|
Mask-fpan: Semi-supervised face parsing in the wild with de-occlusion and uv gan
| null | null | true | false |
Li, Lei and Zhang, Tianfang and Kang, Zhongfeng and Jiang, Xikun
| 2,023 | null | null | null |
Computers \& Graphics
|
Mask-fpan: Semi-supervised face parsing in the wild with de-occlusion and uv gan
|
Mask-FPAN: Semi-Supervised Face Parsing in the Wild ...
|
https://arxiv.org/abs/2212.09098
|
by L Li · 2022 · Cited by 22 — We propose a novel framework termed Mask-FPAN. It uses a de-occlusion module that learns to parse occluded faces in a semi-supervised way.
|
Wav2Sem: Plug-and-Play Audio Semantic Decoupling for 3D Speech-Driven
Facial Animation
|
2505.23290v1
|
haque2023facexhubert
|
\cite{haque2023facexhubert}
|
FaceXHuBERT: Text-less Speech-driven E(X)pressive 3D Facial Animation
Synthesis Using Self-Supervised Speech Representation Learning
|
http://arxiv.org/abs/2303.05416v1
|
This paper presents FaceXHuBERT, a text-less speech-driven 3D facial
animation generation method that allows to capture personalized and subtle cues
in speech (e.g. identity, emotion and hesitation). It is also very robust to
background noise and can handle audio recorded in a variety of situations (e.g.
multiple people speaking). Recent approaches employ end-to-end deep learning
taking into account both audio and text as input to generate facial animation
for the whole face. However, scarcity of publicly available expressive audio-3D
facial animation datasets poses a major bottleneck. The resulting animations
still have issues regarding accurate lip-synching, expressivity,
person-specific information and generalizability. We effectively employ
self-supervised pretrained HuBERT model in the training process that allows us
to incorporate both lexical and non-lexical information in the audio without
using a large lexicon. Additionally, guiding the training with a binary emotion
condition and speaker identity distinguishes the tiniest subtle facial motion.
We carried out extensive objective and subjective evaluation in comparison to
ground-truth and state-of-the-art work. A perceptual user study demonstrates
that our approach produces superior results with respect to the realism of the
animation 78% of the time in comparison to the state-of-the-art. In addition,
our method is 4 times faster eliminating the use of complex sequential models
such as transformers. We strongly recommend watching the supplementary video
before reading the paper. We also provide the implementation and evaluation
codes with a GitHub repository link.
| true | true |
Haque, Kazi Injamamul and Yumak, Zerrin
| 2,023 | null | null | null | null |
FaceXHuBERT: Text-less Speech-driven E(X)pressive 3D Facial Animation
Synthesis Using Self-Supervised Speech Representation Learning
|
Text-less Speech-driven E(X)pressive 3D Facial Animation ...
|
https://www.researchgate.net/publication/372492333_FaceXHuBERT_Text-less_Speech-driven_EXpressive_3D_Facial_Animation_Synthesis_Using_Self-Supervised_Speech_Representation_Learning
|
This paper presents FaceXHuBERT, a text-less speech-driven 3D facial animation generation method that allows us to capture facial cues related to emotional
|
Wav2Sem: Plug-and-Play Audio Semantic Decoupling for 3D Speech-Driven
Facial Animation
|
2505.23290v1
|
EMOTE
|
\cite{EMOTE}
|
Emotional Speech-Driven Animation with Content-Emotion Disentanglement
|
http://arxiv.org/abs/2306.08990v2
|
To be widely adopted, 3D facial avatars must be animated easily,
realistically, and directly from speech signals. While the best recent methods
generate 3D animations that are synchronized with the input audio, they largely
ignore the impact of emotions on facial expressions. Realistic facial animation
requires lip-sync together with the natural expression of emotion. To that end,
we propose EMOTE (Expressive Model Optimized for Talking with Emotion), which
generates 3D talking-head avatars that maintain lip-sync from speech while
enabling explicit control over the expression of emotion. To achieve this, we
supervise EMOTE with decoupled losses for speech (i.e., lip-sync) and emotion.
These losses are based on two key observations: (1) deformations of the face
due to speech are spatially localized around the mouth and have high temporal
frequency, whereas (2) facial expressions may deform the whole face and occur
over longer intervals. Thus, we train EMOTE with a per-frame lip-reading loss
to preserve the speech-dependent content, while supervising emotion at the
sequence level. Furthermore, we employ a content-emotion exchange mechanism in
order to supervise different emotions on the same audio, while maintaining the
lip motion synchronized with the speech. To employ deep perceptual losses
without getting undesirable artifacts, we devise a motion prior in the form of
a temporal VAE. Due to the absence of high-quality aligned emotional 3D face
datasets with speech, EMOTE is trained with 3D pseudo-ground-truth extracted
from an emotional video dataset (i.e., MEAD). Extensive qualitative and
perceptual evaluations demonstrate that EMOTE produces speech-driven facial
animations with better lip-sync than state-of-the-art methods trained on the
same data, while offering additional, high-quality emotional control.
| true | true |
Dan{\v{e}}{\v{c}}ek, Radek and Chhatre, Kiran and Tripathi, Shashank and Wen, Yandong and Black, Michael and Bolkart, Timo
| 2,023 | null | null | null | null |
Emotional Speech-Driven Animation with Content-Emotion Disentanglement
|
Emotional Speech-Driven Animation with Content-Emotion ...
|
https://dl.acm.org/doi/10.1145/3610548.3618183
|
We propose EMOTE (Expressive Model Optimized for Talking with Emotion), which generates 3D talking-head avatars that maintain lip-sync from speech.
|
Wav2Sem: Plug-and-Play Audio Semantic Decoupling for 3D Speech-Driven
Facial Animation
|
2505.23290v1
|
peng2023emotalk
|
\cite{peng2023emotalk}
|
EmoTalk: Speech-Driven Emotional Disentanglement for 3D Face Animation
|
http://arxiv.org/abs/2303.11089v2
|
Speech-driven 3D face animation aims to generate realistic facial expressions
that match the speech content and emotion. However, existing methods often
neglect emotional facial expressions or fail to disentangle them from speech
content. To address this issue, this paper proposes an end-to-end neural
network to disentangle different emotions in speech so as to generate rich 3D
facial expressions. Specifically, we introduce the emotion disentangling
encoder (EDE) to disentangle the emotion and content in the speech by
cross-reconstructed speech signals with different emotion labels. Then an
emotion-guided feature fusion decoder is employed to generate a 3D talking face
with enhanced emotion. The decoder is driven by the disentangled identity,
emotional, and content embeddings so as to generate controllable personal and
emotional styles. Finally, considering the scarcity of the 3D emotional talking
face data, we resort to the supervision of facial blendshapes, which enables
the reconstruction of plausible 3D faces from 2D emotional data, and contribute
a large-scale 3D emotional talking face dataset (3D-ETF) to train the network.
Our experiments and user studies demonstrate that our approach outperforms
state-of-the-art methods and exhibits more diverse facial movements. We
recommend watching the supplementary video:
https://ziqiaopeng.github.io/emotalk
| true | true |
Peng, Ziqiao and Wu, Haoyu and Song, Zhenbo and Xu, Hao and Zhu, Xiangyu and He, Jun and Liu, Hongyan and Fan, Zhaoxin
| 2,023 | null | null | null | null |
EmoTalk: Speech-Driven Emotional Disentanglement for 3D Face Animation
|
Speech-Driven Emotional Disentanglement for 3D Face Animation
|
https://arxiv.org/abs/2303.11089
|
This paper proposes an end-to-end neural network to disentangle different emotions in speech so as to generate rich 3D facial expressions.
|
Wav2Sem: Plug-and-Play Audio Semantic Decoupling for 3D Speech-Driven
Facial Animation
|
2505.23290v1
|
thambiraja20233diface
|
\cite{thambiraja20233diface}
|
3DiFACE: Diffusion-based Speech-driven 3D Facial Animation and Editing
|
http://arxiv.org/abs/2312.00870v1
|
We present 3DiFACE, a novel method for personalized speech-driven 3D facial
animation and editing. While existing methods deterministically predict facial
animations from speech, they overlook the inherent one-to-many relationship
between speech and facial expressions, i.e., there are multiple reasonable
facial expression animations matching an audio input. It is especially
important in content creation to be able to modify generated motion or to
specify keyframes. To enable stochasticity as well as motion editing, we
propose a lightweight audio-conditioned diffusion model for 3D facial motion.
This diffusion model can be trained on a small 3D motion dataset, maintaining
expressive lip motion output. In addition, it can be finetuned for specific
subjects, requiring only a short video of the person. Through quantitative and
qualitative evaluations, we show that our method outperforms existing
state-of-the-art techniques and yields speech-driven animations with greater
fidelity and diversity.
| true | true |
Balamurugan Thambiraja and
Sadegh Aliakbarian and
Darren Cosker and
Justus Thies
| 2,023 | null | null | null |
CoRR
|
3DiFACE: Diffusion-based Speech-driven 3D Facial Animation and Editing
|
[2312.00870] 3DiFACE: Diffusion-based Speech-driven 3D ...
|
https://arxiv.org/abs/2312.00870
|
by B Thambiraja · 2023 · Cited by 18 — Abstract:We present 3DiFACE, a novel method for personalized speech-driven 3D facial animation and editing.
|
Wav2Sem: Plug-and-Play Audio Semantic Decoupling for 3D Speech-Driven
Facial Animation
|
2505.23290v1
|
VOCA
|
\cite{VOCA}
|
Capture, Learning, and Synthesis of 3D Speaking Styles
|
http://arxiv.org/abs/1905.03079v1
|
Audio-driven 3D facial animation has been widely explored, but achieving
realistic, human-like performance is still unsolved. This is due to the lack of
available 3D datasets, models, and standard evaluation metrics. To address
this, we introduce a unique 4D face dataset with about 29 minutes of 4D scans
captured at 60 fps and synchronized audio from 12 speakers. We then train a
neural network on our dataset that factors identity from facial motion. The
learned model, VOCA (Voice Operated Character Animation) takes any speech
signal as input - even speech in languages other than English - and
realistically animates a wide range of adult faces. Conditioning on subject
labels during training allows the model to learn a variety of realistic
speaking styles. VOCA also provides animator controls to alter speaking style,
identity-dependent facial shape, and pose (i.e. head, jaw, and eyeball
rotations) during animation. To our knowledge, VOCA is the only realistic 3D
facial animation model that is readily applicable to unseen subjects without
retargeting. This makes VOCA suitable for tasks like in-game video, virtual
reality avatars, or any scenario in which the speaker, speech, or language is
not known in advance. We make the dataset and model available for research
purposes at http://voca.is.tue.mpg.de.
| true | true |
Daniel Cudeiro and
Timo Bolkart and
Cassidy Laidlaw and
Anurag Ranjan and
Michael J. Black
| 2,019 | null | null | null | null |
Capture, Learning, and Synthesis of 3D Speaking Styles
|
Capture, Learning, and Synthesis of 3D Speaking Styles
|
http://arxiv.org/pdf/1905.03079v1
|
Audio-driven 3D facial animation has been widely explored, but achieving
realistic, human-like performance is still unsolved. This is due to the lack of
available 3D datasets, models, and standard evaluation metrics. To address
this, we introduce a unique 4D face dataset with about 29 minutes of 4D scans
captured at 60 fps and synchronized audio from 12 speakers. We then train a
neural network on our dataset that factors identity from facial motion. The
learned model, VOCA (Voice Operated Character Animation) takes any speech
signal as input - even speech in languages other than English - and
realistically animates a wide range of adult faces. Conditioning on subject
labels during training allows the model to learn a variety of realistic
speaking styles. VOCA also provides animator controls to alter speaking style,
identity-dependent facial shape, and pose (i.e. head, jaw, and eyeball
rotations) during animation. To our knowledge, VOCA is the only realistic 3D
facial animation model that is readily applicable to unseen subjects without
retargeting. This makes VOCA suitable for tasks like in-game video, virtual
reality avatars, or any scenario in which the speaker, speech, or language is
not known in advance. We make the dataset and model available for research
purposes at http://voca.is.tue.mpg.de.
|
Wav2Sem: Plug-and-Play Audio Semantic Decoupling for 3D Speech-Driven
Facial Animation
|
2505.23290v1
|
LG-LDM
|
\cite{LG-LDM}
|
Expressive 3D Facial Animation Generation Based on Local-to-global Latent Diffusion
| null | null | true | false |
Song, Wenfeng and Wang, Xuan and Jiang, Yiming and Li, Shuai and Hao, Aimin and Hou, Xia and Qin, Hong
| 2,024 | null | null | null |
TVCG
|
Expressive 3D Facial Animation Generation Based on Local-to-global Latent Diffusion
|
wangxuanx/Face-Diffusion-Model: The official pytorch code ...
|
https://github.com/wangxuanx/Face-Diffusion-Model
|
Expressive 3D Facial Animation Generation Based on Local-to-global Latent Diffusion ... Our method generates realistic facial animations by syncing lips with
|
Wav2Sem: Plug-and-Play Audio Semantic Decoupling for 3D Speech-Driven
Facial Animation
|
2505.23290v1
|
fu2024mimic
|
\cite{fu2024mimic}
|
Mimic: Speaking Style Disentanglement for Speech-Driven 3D Facial
Animation
|
http://arxiv.org/abs/2312.10877v1
|
Speech-driven 3D facial animation aims to synthesize vivid facial animations
that accurately synchronize with speech and match the unique speaking style.
However, existing works primarily focus on achieving precise lip
synchronization while neglecting to model the subject-specific speaking style,
often resulting in unrealistic facial animations. To the best of our knowledge,
this work makes the first attempt to explore the coupled information between
the speaking style and the semantic content in facial motions. Specifically, we
introduce an innovative speaking style disentanglement method, which enables
arbitrary-subject speaking style encoding and leads to a more realistic
synthesis of speech-driven facial animations. Subsequently, we propose a novel
framework called \textbf{Mimic} to learn disentangled representations of the
speaking style and content from facial motions by building two latent spaces
for style and content, respectively. Moreover, to facilitate disentangled
representation learning, we introduce four well-designed constraints: an
auxiliary style classifier, an auxiliary inverse classifier, a content
contrastive loss, and a pair of latent cycle losses, which can effectively
contribute to the construction of the identity-related style space and
semantic-related content space. Extensive qualitative and quantitative
experiments conducted on three publicly available datasets demonstrate that our
approach outperforms state-of-the-art methods and is capable of capturing
diverse speaking styles for speech-driven 3D facial animation. The source code
and supplementary video are publicly available at:
https://zeqing-wang.github.io/Mimic/
| true | true |
Hui Fu and
Zeqing Wang and
Ke Gong and
Keze Wang and
Tianshui Chen and
Haojie Li and
Haifeng Zeng and
Wenxiong Kang
| 2,024 | null | null | null | null |
Mimic: Speaking Style Disentanglement for Speech-Driven 3D Facial
Animation
|
[PDF] Speaking Style Disentanglement for Speech-Driven 3D Facial ...
|
https://ojs.aaai.org/index.php/AAAI/article/view/27945/27910
|
We propose Mimic for style-content disentanglement and synthesizing facial animations matching an identity-specific speaking style, as illustrated in Figure 2.
|
Wav2Sem: Plug-and-Play Audio Semantic Decoupling for 3D Speech-Driven
Facial Animation
|
2505.23290v1
|
wav2lip
|
\cite{wav2lip}
|
A Lip Sync Expert Is All You Need for Speech to Lip Generation In The
Wild
|
http://arxiv.org/abs/2008.10010v1
|
In this work, we investigate the problem of lip-syncing a talking face video
of an arbitrary identity to match a target speech segment. Current works excel
at producing accurate lip movements on a static image or videos of specific
people seen during the training phase. However, they fail to accurately morph
the lip movements of arbitrary identities in dynamic, unconstrained talking
face videos, resulting in significant parts of the video being out-of-sync with
the new audio. We identify key reasons pertaining to this and hence resolve
them by learning from a powerful lip-sync discriminator. Next, we propose new,
rigorous evaluation benchmarks and metrics to accurately measure lip
synchronization in unconstrained videos. Extensive quantitative evaluations on
our challenging benchmarks show that the lip-sync accuracy of the videos
generated by our Wav2Lip model is almost as good as real synced videos. We
provide a demo video clearly showing the substantial impact of our Wav2Lip
model and evaluation benchmarks on our website:
\url{cvit.iiit.ac.in/research/projects/cvit-projects/a-lip-sync-expert-is-all-you-need-for-speech-to-lip-generation-in-the-wild}.
The code and models are released at this GitHub repository:
\url{github.com/Rudrabha/Wav2Lip}. You can also try out the interactive demo at
this link: \url{bhaasha.iiit.ac.in/lipsync}.
| true | true |
K. R. Prajwal and
Rudrabha Mukhopadhyay and
Vinay P. Namboodiri and
C. V. Jawahar
| 2,020 | null | null | null | null |
A Lip Sync Expert Is All You Need for Speech to Lip Generation In The
Wild
|
[2008.10010] A Lip Sync Expert Is All You Need for Speech ...
|
https://arxiv.org/abs/2008.10010
|
**arXiv:2008.10010** (cs) View a PDF of the paper titled A Lip Sync Expert Is All You Need for Speech to Lip Generation In The Wild, by K R Prajwal and 3 other authors (or arXiv:2008.10010v1 [cs.CV] for this version) View a PDF of the paper titled A Lip Sync Expert Is All You Need for Speech to Lip Generation In The Wild, by K R Prajwal and 3 other authors - [x] Bibliographic Explorer Toggle - [x] Connected Papers Toggle - [x] Litmaps Toggle - [x] alphaXiv Toggle - [x] Links to Code Toggle - [x] DagsHub Toggle - [x] GotitPub Toggle - [x] Links to Code Toggle - [x] ScienceCast Toggle - [x] Replicate Toggle - [x] Core recommender toggle
|
Wav2Sem: Plug-and-Play Audio Semantic Decoupling for 3D Speech-Driven
Facial Animation
|
2505.23290v1
|
DBLP:conf/bmvc/ChenLLYW21
|
\cite{DBLP:conf/bmvc/ChenLLYW21}
|
Talking Head Generation with Audio and Speech Related Facial Action
Units
| null | null | true | false |
Sen Chen and
Zhilei Liu and
Jiaxing Liu and
Zhengxiang Yan and
Longbiao Wang
| 2,021 | null | null | null | null |
Talking Head Generation with Audio and Speech Related Facial Action
Units
|
Talking Head Generation with Audio and Speech Related Facial ...
|
https://arxiv.org/abs/2110.09951
|
In this paper, we propose a novel recurrent generative network that uses both audio and speech-related facial action units (AUs) as the driving information.
|
Wav2Sem: Plug-and-Play Audio Semantic Decoupling for 3D Speech-Driven
Facial Animation
|
2505.23290v1
|
DeepSpeech
|
\cite{DeepSpeech}
|
Deep Speech: Scaling up end-to-end speech recognition
|
http://arxiv.org/abs/1412.5567v2
|
We present a state-of-the-art speech recognition system developed using
end-to-end deep learning. Our architecture is significantly simpler than
traditional speech systems, which rely on laboriously engineered processing
pipelines; these traditional systems also tend to perform poorly when used in
noisy environments. In contrast, our system does not need hand-designed
components to model background noise, reverberation, or speaker variation, but
instead directly learns a function that is robust to such effects. We do not
need a phoneme dictionary, nor even the concept of a "phoneme." Key to our
approach is a well-optimized RNN training system that uses multiple GPUs, as
well as a set of novel data synthesis techniques that allow us to efficiently
obtain a large amount of varied data for training. Our system, called Deep
Speech, outperforms previously published results on the widely studied
Switchboard Hub5'00, achieving 16.0% error on the full test set. Deep Speech
also handles challenging noisy environments better than widely used,
state-of-the-art commercial speech systems.
| true | true |
Awni Y. Hannun and
Carl Case and
Jared Casper and
Bryan Catanzaro and
Greg Diamos and
Erich Elsen and
Ryan Prenger and
Sanjeev Satheesh and
Shubho Sengupta and
Adam Coates and
Andrew Y. Ng
| 2,014 | null | null | null |
CoRR
|
Deep Speech: Scaling up end-to-end speech recognition
|
[PDF] Deep Speech: Scaling up end-to-end speech recognition - arXiv
|
https://arxiv.org/pdf/1412.5567
|
Deep Speech is an end-to-end speech recognition system using deep learning, a simpler architecture, and a large RNN trained with multiple GPUs.
|
Wav2Sem: Plug-and-Play Audio Semantic Decoupling for 3D Speech-Driven
Facial Animation
|
2505.23290v1
|
wav2vec
|
\cite{wav2vec}
|
wav2vec 2.0: A Framework for Self-Supervised Learning of Speech
Representations
|
http://arxiv.org/abs/2006.11477v3
|
We show for the first time that learning powerful representations from speech
audio alone followed by fine-tuning on transcribed speech can outperform the
best semi-supervised methods while being conceptually simpler. wav2vec 2.0
masks the speech input in the latent space and solves a contrastive task
defined over a quantization of the latent representations which are jointly
learned. Experiments using all labeled data of Librispeech achieve 1.8/3.3 WER
on the clean/other test sets. When lowering the amount of labeled data to one
hour, wav2vec 2.0 outperforms the previous state of the art on the 100 hour
subset while using 100 times less labeled data. Using just ten minutes of
labeled data and pre-training on 53k hours of unlabeled data still achieves
4.8/8.2 WER. This demonstrates the feasibility of speech recognition with
limited amounts of labeled data.
| true | true |
Alexei Baevski and
Yuhao Zhou and
Abdelrahman Mohamed and
Michael Auli
| 2,020 | null | null | null | null |
wav2vec 2.0: A Framework for Self-Supervised Learning of Speech
Representations
|
wav2vec 2.0: A Framework for Self-Supervised Learning of Speech ...
|
https://arxiv.org/abs/2006.11477
|
wav2vec 2.0 masks the speech input in the latent space and solves a contrastive task defined over a quantization of the latent representations
|
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.