Update README.md

README.md

@@ -11,58 +11,43 @@ tags:
   - vision-language
 ---
 
-# RA-HMD Dataset
+# RGCL Dataset Resources
 
-This repository contains the dataset for the paper [Robust Adaptation of Large Multimodal Models for Retrieval Augmented Hateful Meme Detection](https://huggingface.co/papers/2502.13061).
+This repository contains the dataset for the paper [Improving Hateful Meme Detection through Retrieval-Guided Contrastive Learning](https://aclanthology.org/2024.acl-long.291).
 
-This dataset supports the development of robust automated detection systems for hateful memes. It is designed to enhance in-domain accuracy and cross-domain generalization for Large Multimodal Models (LMMs) while preserving their general vision-language capabilities. The data provided includes the original datasets and a converted format suitable for use with the [LLaMA-Factory](https://github.com/hiyouga/LLaMA-Factory) framework for stage 1 training of the RA-HMD model.
+This provides the sparse retrieval dataset for the RGCL paper. 
 
 For more details and related resources:
--   **Paper**: [Robust Adaptation of Large Multimodal Models for Retrieval Augmented Hateful Meme Detection](https://huggingface.co/papers/2502.13061)
+-   **Paper**: [Improving Hateful Meme Detection through Retrieval-Guided Contrastive Learning](https://aclanthology.org/2024.acl-long.291)
 -   **Code (GitHub)**: https://github.com/JingbiaoMei/RGCL
 -   **Project Page**: https://rgclmm.github.io/
 
-### Sample Usage
-
-The following instructions are derived from the [GitHub repository](https://github.com/JingbiaoMei/RGCL) and show how to set up the environment and generate embeddings for the dataset.
-
-#### Setup Environment for RA-HMD
-
-```bash
-git clone https://github.com/JingbiaoMei/RGCL.git
-cd RGCL/LLAMA-FACTORY
-conda create -n llamafact python=3.10
-conda activate llamafact
-pip install -e ".[torch,metrics,deepspeed,liger-kernel,bitsandbytes,qwen]"
-pip install torchmetrics wandb easydict
-pip install qwen_vl_utils torchvision
-# Install FAISS
-conda install -c pytorch -c nvidia faiss-gpu=1.7.4 mkl=2021 blas=1.0=mkl
-```
-
-#### Dataset Preparation - Generate CLIP Embedding
-
-First, ensure image data is copied into `./data/image/dataset_name/All` and annotation data (`jsonl`) into `./data/gt/dataset_name`. Then, generate CLIP embeddings:
-
-```shell
-python3 src/utils/generate_CLIP_embedding_HF.py --dataset "FB"
-python3 src/utils/generate_CLIP_embedding_HF.py --dataset "HarMeme"
-```
-
-#### Dataset Preparation - Generate ALIGN Embedding
-
-Similarly, generate ALIGN embeddings:
-
-```shell
-python3 src/utils/generate_ALIGN_embedding_HF.py --dataset "FB"
-python3 src/utils/generate_ALIGN_embedding_HF.py --dataset "HarMeme"
-```
 
 ### Citation
 
 If you use this dataset in your research, please kindly cite the corresponding paper:
 
 ```bibtex
+@inproceedings{RGCL2024Mei,
+    title = "Improving Hateful Meme Detection through Retrieval-Guided Contrastive Learning",
+    author = "Mei, Jingbiao  and
+      Chen, Jinghong  and
+      Lin, Weizhe  and
+      Byrne, Bill  and
+      Tomalin, Marcus",
+    editor = "Ku, Lun-Wei  and
+      Martins, Andre  and
+      Srikumar, Vivek",
+    booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
+    month = aug,
+    year = "2024",
+    address = "Bangkok, Thailand",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2024.acl-long.291",
+    doi = "10.18653/v1/2024.acl-long.291",
+    pages = "5333--5347"
+}
+
 @article{RAHMD2025Mei,
     title={Robust Adaptation of Large Multimodal Models for Retrieval Augmented Hateful Meme Detection},
     url={http://arxiv.org/abs/2502.13061},