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Explore Inter-contrast between Videos via Composition for Weakly Supervised Temporal Sentence Grounding

Weakly supervised temporal sentence grounding aims to tem- porally localize the target segment corresponding to a given natural language query, where it provides video-query pairs without temporal annotations during training. Most existing methods use the fused visual-linguistic feature to re- construct the query, where the least reconstruction error de-termines the target segment. This work introduces a novel approach that explores the inter-contrast between videos in a composed video by selecting components from two dif- ferent videos and fusing them into a single video. Such a straightforward yet effective composition strategy provides the temporal annotations at multiple composed positions, resulting in numerous videos with temporal ground-truths for training the temporal sentence grounding task. A transformer framework is introduced with multi-tasks training to learn a compact but efﬁcient visual-linguistic space. The experi-mental results on the public Charades-STA and ActivityNet- Caption dataset demonstrate the effectiveness of the proposed method, where our approach achieves comparable per- formance over the state-of-the-art weakly-supervised base-lines. The code is available at https://github.com/PPjmchen/ Composition WSTG.


Introduction
Temporal sentence grounding (Gao et al. 2017) is regarded as a crucial vision-language task that aims at localizing the temporal boundary of the target segment in an untrimmed video when given a natural language query. As a fundamental problem in video analysis and understanding, it has attracted increasing research interest over the last few years.
Many fully supervised methods (Anne Hendricks et al. 2017;Mithun, Paul, and Roy-Chowdhury 2019;Yuan, Mei, and Zhu 2019;Yuan et al. 2020;Wang, Ma, and Jiang 2020;Zhang et al. 2020aZhang et al. , 2021 has been developed, which directly learn from the videos, the text queries, and the temporal boundaries. Such methods rely on a variety of multimodal information fusion and supervision from accuracy timestamp labels to model the boundary and content information of the target segments. However, the fully supervised setting needs precise temporal boundary annotations that are expensive and time-consuming. This inevitable limitation prevents the temporal sentence grounding from being applied at a large scale. The limitations come from the precise but labor-intensive boundary annotations, while the text query annotations are much easier to collect . To this end, the weakly supervised temporal sentence grounding setting is introduced, where coarse text-video pair annotations are provided only during training. Without boundary annotations of the target segments, recent methods Song et al. 2020) fuse the visual and linguistic features and reconstruct either themselves or the masked query. The target segment accompanied by the text query is expected to yield the least reconstruction error during testing. These methods, however, cannot guarantee that the target segments always correspond to the best reconstruction due to the powerful representation of deep neural networks that can even well reconstruct the irrelevant clips.
Videos describe a sequence of actions accompanied by various participants and complex backgrounds, leading to a contrast of these elements among different videos. We name it inter-contrast in this paper. Due to camera movements and scene transitions, one video can be treated as a combination of a series of clips, which undoubtedly brings temporal correlations and intra-contrast between these clips, as shown in Figure 1 (a). Inspired by this observation, we intend to use the composition of clips from different videos to simulate a collection of clips inside a natural video. As shown in Figure 1 (b), the built-up video-video pairs can be regarded as the simulation of clip-clip pairs inside a single video. Under the weakly supervised setting, this approach can generate a series of re-organized samples with pseudo-labels of temporal boundaries for training. That means we can easily tackle the weakly supervised setting by the fully supervised view. Suppose the model can learn the inter-contrast between clips sampled from different videos and successfully locates all the clips based on their natural language descriptions. In that case, such a mechanism can be seamlessly applied to a regular testing video to locate the target segment given the corresponding text query. We propose a novel method for weakly supervised temporal sentence grounding via the proposed video composition strategy based on this assumption.
The key idea of this paper is that the training videos are composed of different videos, yielding numerous and precise composition timestamps. With the crafted video, we further carefully investigate how to separate it into different semantic components for different text queries. Given one of the text queries from the two sampled videos, the composed video can be treated as a collection of text-related and text-unrelated segments. The former explicitly indicates several temporal annotations corresponding to the given query. We then reverse the temporal annotations for the other query text. Thus, it provides supervision signals to train the weakly supervised temporal sentence grounding task. As shown in Figure 1 (b), we can create a reliable synthetic training sample with precise temporal boundaries. The network takes it as input and performs temporal grounding by learning to separate the composed video to different semantic components conditioned on the respective texts. Besides, a negative query sample is introduced, which is critical to prevent the model from learning to locate the target segments using the obvious scene transitions between different videos. To effectively capture rich semantics in the video and text query as well as encode videos and texts into a unified sequence representation, we utilize a transformer-based model adopted in (Zhang et al. 2021), which is trained by the above three queries together.
To summarize, our main contributions are three-fold: i) we tackle the weakly supervised temporal sentence grounding task with a novel approach based on learning to ground segments composed from different videos in training, where such composition brings pseudo temporal annotations for training; ii) we propose a multi-queries grounding task in the fully supervised manner where it recognizes the composed video from different query sentences, predicts composition ratios in a global view and predicts masked word prediction task to better align visual-linguistic features. All these tasks are seamlessly integrated into an emerging transformer-based model; iii) our method achieves compara-ble performance over the state-of-the-art weakly-supervised approaches (Gao et al. 2019;Lin et al. 2020;Song et al. 2020;Wu et al. 2020;Zhang et al. 2020c;Ma et al. 2020;Tan et al. 2021;Zhang et al. 2020b) on the public Charades-STA and ActivityNet-Captions dataset.

Related Work Fully Supervised Temporal Grounding
As one of multi-modalities tasks (Zhang et al. 2019b;Arbelle et al. 2021;Zhang et al. 2021;Wang et al. 2018Wang et al. , 2019, temporal sentence grounding is a task to temporally localize the corresponding segment in a video based on a given sentence query, initially proposed by (Gao et al. 2017;Anne Hendricks et al. 2017). Until now, it remains challenging since it requires understanding the semantics of both vision and language and realizing alignment between these two modalities. Early work (Gao et al. 2017) utilizes sliding windows to extract candidate video segments. The segment features are extracted and then fused with text representation by various multi-modal fusion operations (i.e., Add, Multiply, and Fully-connected) for estimating the correlation between queries and clips. (Anne Hendricks et al. 2017) proposes to minimize the squared distance between candidate clips and query sentences in the joint representation space. The two early methods follow a simple pipeline of extracting a set of candidate clips and then matching the clip and the given query. The following work adopts more sophisticated strategies such as Graph Convolutional Network used in (Zhang et al. 2019a), attention mechanisms used in (Yuan, Mei, and Zhu 2019), and reinforcement learning used in (He et al. 2019). In addition, (Wang, Ma, and Jiang 2020) introduces a boundary-aware method that keeps the paradigm of candidate matching and proposes an additional branch to predict the temporal boundaries of the target segments. 2D Temporal Adjacent Network (Zhang et al. 2020a) proposes to embed segment representation and language representation into a 2D feature map and utilizes 2D convolutions to align these two modalities and extract adjacent relationships among candidate segments. Inspired by recently proposed multi-modal BERT models, (Zhang et al. 2021) presents a visual-language transformer backbone and utilizes multi-stage feature aggregation to produce discriminative representation for temporal sentence grounding.

Weakly Supervised Temporal Grounding
Although temporal sentence grounding methods succeed under the fully supervised setting, they rely on laborintensive labeling. In contrast, weakly supervised temporal sentence grounding learns from coarse video-query pairs without timestamp ground truths. In (Mithun, Paul, and Roy-Chowdhury 2019), a text guided attention module (TGA) is proposed to extract frame-wise attention scores, and sliding windows with different sizes are utilized to aggregate segment attention scores. The segments with the highest matching scores are considered as predictions in the testing stage. (Tan et al. 2019) proposes a multi-level co-attention mechanism for multi-modal alignment and utilizes the positional encoding to construct the temporally-aware multi-modal representations. (Gao et al. 2019) designs a multiinstance learning framework consisting of a classification module and a ranking module. The former measures the matching scores between candidate segments and query sentences, while the latter intends to rank and select the candidates. Inspired by sentence reconstruction in visual grounding , (Song et al. 2020) proposes to reconstruct the whole sentence from weighted features of candidate video segments.  proposes to reconstruct the masked keywords of the query sentence from the candidate video segments, as the reconstruction loss will guide the network to discover high-quality proposal segments by assigning higher confidence scores to them. ) proposes a Video-Language Alignment Network (VLANet) based on contrastive learning. Firstly, it utilizes the Surrogate Proposal Selection module to select candidate proposals in a multi-modal representation space. Then the Cascaded Cross-modal Attention module learns to align two modalities based on feature interactions and multi-directional attention flows. (Wu et al. 2020) proposes a Boundary Adaptive Refinement (BAR) network based on reinforcement learning, which designs a cross-modal evaluator for computing the matching scores between video segments and queries and providing boundary-flexible and content-aware rewards. (Zhang et al. 2020b) proposes a Regularized Two-Branch Proposal Network (RTBPN), which designs a language-aware filter to generate an enhanced video stream and a suppressed one and utilizes a two-branch framework to learn from inter-sample and intra-sample confrontments simultaneously. (Tan et al. 2021) leverages a multi-level co-attention mechanism and proposes a Latent Graph Co-Attention Network (LoGAN). The network extracts the context cues from all possible pairs of frames through frame-by-word interactions and learns contextualized visual-semantic representations.

Proposed Method Problem Formulation
Mathematically, given a set of video-query pairs P test = {(V 1 , Q 1 ), (V 2 , Q 2 ), ..., (V N , Q N )}, the weakly supervised temporal sentence grounding task intends to predict the temporal range {(s 1 , e 1 ), (s 2 , e 2 ), ..., (s N , e N )} of the target segments. Here s i and e i denote the start timestamp and the end timestamp of the i-th target segment and N denotes the number of pairs in the testing set. During training, it provides another set of video-query pairs Here M represents the number of pairs in the training set and P train ∩ P test = ∅.
Following (Zhang et al. 2020a(Zhang et al. , 2021, each video is evenly divided into N C clips. The feature vectors in each clip are extracted from a pre-trained CNN model and averaged across time dimensions, resulting in a single clip feature vector with a dimension of D V . A downsampling 1D average pooling layer is applied over all the clip feature vectors so that each video can be represented to Similarly, we use a pre-trained GloVe (Pennington, Socher, and Manning 2014) model to embed each word in the query sentence and pad them to a fixed length N Q , which can be denoted as The following sections are organized as follows. First, our used temporal sentence grounding framework is introduced. Afterward, we present the proposed composition strategy, including composing input videos and generating pseudolabels for training the network in detail. Last but not least, the training losses and the inference details are illustrated.

Temporal Sentence Grounding Framework
Since our proposed composition strategy naturally provides pseudo labels for training, any advanced, fully supervised method can be seamlessly integrated into our framework. This paper constructs our approach based on the cuttingedge transformer introduced.
Recently, a multi-modal transformer (Su et al. 2019;Chen et al. 2020;Lin et al. 2020;Zhang et al. 2021) is proposed to address a series of vision-language tasks. Such networks have a similar framework as shown in Figure 2, where the visual data and text data are embedded into an inputsequence of feature tokens that interact inside the multi-head self-attention layers of the transformer network. Thus, the produced output-sequence aggregates cross-modalities information. We then introduce the technical details of such a robust network. More specifically, the visual features and the text features are mapped to the same dimension, where each of them is regarded as a feature token. We then put them together as the sequence inputs added with the standard position embedding. As discussed in (Zhang et al. 2021), to better model different modalities, the parameters of the self-attention layers are decoupled to two groups for visual and language, respectively. In the process of interaction inside the self-attention layers, two modalities are fused in joint representation space. Similar to other multi-modal transformers, the output sequence is divided back into two groups, which corresponds to the original visual sequence and text sequence as shown in Figure 2.
To our best knowledge, this is the first work to tackle the weakly supervised temporal sentence grounding in a fully supervised manner. We modify the multi-modal transformer (Zhang et al. 2021) by introducing video composition and pseudo-label generation, including query pseudolabels, localization pseudo-labels, and video-query alignment pseudo-labels. It turns out that our method not only intends to align the visual target segment and the linguistic query but also explores the inter-contrast between videos due to the composition.

Composition Strategy Video Composition
In this work, we propose a video composition method to produce artificial video samples for training. There exist the inter-contrast between different videos naturally. We assume that the model can well learn the visual-linguistic corresponding within a regular video as long as it has successfully captured the counterpart within a composed video. That means we replace the investigation of intra-contrast within a single video with the exploration of inter-contrast

Composed Video
Positional Embedding Figure 2: The main framework of the proposed method. Two video-query pairs are randomly sampled from the training set, and the videos are broken into clips and composed randomly. The visual and text sequences are mapped to the same dimension feature vectors and put together and fed into the transformer encoder. Conditioned by different queries, the output sequence is aggregated in the prediction head, which performs multi-task learning: boundary prediction, composition ratio prediction and masked word prediction.
between different videos when temporal annotations related to queries are absent in the training set. More specifically, as shown in Figure 1 (b), two video-query pairs (V 1 , Q 1 ), (V 2 , Q 2 ) are sampled randomly from the training set. Recall each video holds the same length N V . Therefore we randomly select a clip in the second video and its length ratio l accounts for the whole video and satisfies r 1 ≤ l ≤ r 2 , where 0 < r 1 , r 2 < 1 are hyperparameters empirically selected. The selected clip is inserted into another random position of the first sampled video. It should be noticed that the two queries remain the same.

Pseudo-Labels Generation
As shown in Figure 2, composing videos naturally produces artificial videos with clear boundary information, which is absent in most of existing weakly supervised temporal sentence grounding methods. Our framework provides three kinds of pseudo-labels for training a network: Query Pseudo-Labels Aforementioned, contrast between clips always happens within a regular video due to camera movements and scene transitions. This property can be easily simulated via video composition because of the intercontrast between different videos. During the composition of two different videos from the training set, the two corresponding queries remain the same. Meanwhile, we randomly choose a query sentence corresponding to the third video not involved in composition. Conditioned by these three query sentences, the network should yield different responses. We train the network with the first two query sentences relevant to the composed video as the fully supervised setting always does.
It is worth noting that inserting V 2 clips will interrupt V 1 and split V 1 into two parts in the composed video where the Q 1 is used to recover the starting and ending timestamps twice. The network, however, may easily locate the target segments due to the obvious boundary artifacts in the composed videos. To this end, we also add the irrelevant query as a regularization used in (Arbelle et al. 2021), in which the network should refuse to respond when it takes the irrelevant query as an input.
Localization Pseudo-Labels Besides target classification and boundary localization commonly used in many grounding tasks, the previous works (Wang, Ma, and Jiang 2020;Zhang et al. 2021) propose to carry out classification on moments such as starting time and ending time, where it can contribute to the grounding scores. Thus, we construct the localization pseudo-labels by each clip's temporal boundaries, i.e. starting timestamp t s and ending timestamp t e , as well as their middle timestamp t m = ts+te 2 . Rather than producing a single positive label at each moment, we generate the moment classification ground-truths with multiple Gaussian distributions following (Zhang et al. 2021). Specifically, for a clip spanning over [t s , t e ], the moment classification ground-truths d s , d e , and d m of the t-th output visual feature in the transformer encoder can be formulated as follows: Here the range of t s , t e is normalized to [0, N C ]. The σ = α(t s − t e ) is the deviation of the Gaussian distribution and the α denotes the controlling factor for sharpness. Such localization pseudo-labels from the composed videos will be verified in experiments where they can effectively guide the network to conduct accurate grounding in regular videos even under the weakly supervised setting.
Video-query Alignment Pseudo-Labels Perform localization for all the composed clips may ignore the global information of the whole composed video. To remedy this problem, we propose to predict the proportions of the clips corresponding to different queries, which requires the network to aggregate all the information to produce the predictions. Concretely, an average operation is carried out over all the visual features, and a fully-connected layer is used to produce proportion scalars, as shown in Figure 2.

Training
As introduced above, three pseudo-labels are constructed from the composed video, and we then design three tasks as follows: (1) Proposal classification and moment classification; (2) Composition ratio prediction; (3) Masked word prediction. For simplicity, we sample two different videoquery pairs (V a , Q a ) and (V b , Q b ) to compose a video, and we also sample another video-query pair (V n , Q n ) as a negative sample. Since we paste a clip C b randomly sampled from V b into V a , the composed video consists of a clip from V b and two clips from V a . Here we take the C b with the query Q b as an example for simplicity.

Proposal Classification and Moment Classification
The temporal sentence grounding intends to temporally locate the target segment related to the query, which can be regarded as a classification task and a localization task. Thus, we follow the proposed classification and the moment classification used in (Zhang et al. 2021). Initially, video segment proposals are proposed in a sparse way introduced in (Zhang et al. 2020a). Those proposals of which Intersection over Union(IoU) with C b is higher than a predefined threshold t h are treated as positive samples, and those of which IoU is below than another predefined threshold t l are treated as negative samples during training. Other proposals that do not satisfy these requirements will not participate in training. The objective function designed for the proposed classification is defined as follows.
where L CE is a cross-entropy loss andĝ is the proposal score. For Q a and Q b , the ground-truth g is set to 1 for positive proposals, otherwise 0 for negative ones. Conditioned by the negative query Q n , the ground-truth g n is set to all zeros.
As for the moment classification, the model is required to classify the moment for each one of N V steps. The objective function of the moment classification is defined below.
whered s ,d m , andd e are the boundary predictions for the starting moment, the middle moment, and the ending moment, respectively. Meanwhile, the ground-truths d s , d m , and d e have been illustrated in the previous subsection. Conditioned by the negative query Q n , we set the ground-truth d n to all zeros.
Composition Ratio Prediction We also construct a task on a video level. After averaging all the output features, a fully-connected layer is used to predict the ratior of C b in the composed video given Q b . Conditioned by the two relevant queries Q a and Q b , the model should predict their own proportion in the composed video. Conditioned by the negative query Q n , we set the ground-truth r n to all zeros. The loss is designed as follows: Masked Word Prediction To better align visual and linguistic features, we mask each word in Q a , Q b with a probability of 0.15 and use the "[MASK]" token to replace them. For the output feature of the masked words, the transformer should reconstruct the masked words w from other unmasked words and the visual information, which is widely used in other multi-modal transformer frameworks (Zhang et al. 2021) and the standard cross-entropy loss is used here: whereŵ is the predicted masked word.
Total loss During the training phase, we compute the weighted sum for all the losses above as follows: where the w P , w B , w R , w M are hyper-parameters to indicate the importance of each task.

Inference
In the inference stage, the composed video is replaced by the testing video, which is fed to the transformer encoder with the unmasked query sentence together. All of the candidate clips are sorted by the mean of proposal classification and moment classification scores as the prediction results.

Experiments
To verify the effectiveness of our composition strategy, we perform experiments on two public benchmark datasets ActivityNet-Captions (Caba Heilbron et al. 2015) and Charades-STA (Sigurdsson et al. 2016). Since our method is the first to tackle the weakly supervised temporal sentence grounding task in a fully supervised manner, we compare the proposed method with both the recent state-of-theart fully supervised methods MCN (Anne Hendricks et al.     (Zhang et al. 2020b). We also perform ablation study and qualitative analysis in this section.

Datasets
ActivityNet-Captions. The ActivityNet-Captions dataset is built on ActivityNet v1.3 dataset (Caba Heilbron et al. 2015), which is originally developed for human activity understanding. It consists of 19209 videos and is the largest dataset for temporal sentence grounding. Following the splitting in (Zhang et al. 2020a(Zhang et al. , 2021, we use val-1 as the validation set and val-2 as the testing set. In conclusion, we have 37417, 17505, and 17031 moment-sentence pairs for training, validation, and testing, respectively. Charades-STA. The Charades-STA dataset is built upon Charades (Sigurdsson et al. 2016) by adding query sentence annotations, which are all about indoor activities. (Gao et al. 2017) proposes a semi-automatic method to generate moment-query annotations for fully supervised temporal sentence grounding. Following the splitting as , 12408 video-query pairs are used for training and 3720 pairs for testing in our experiments.

Evaluation Metric
Following the evaluation metric proposed in (Gao et al. 2017), we adopt the "R@n, IoU=m" metric to evaluate the performance for all methods. Specifically, it computes the percentage of testing samples that have at least one correct grounding prediction (i.e., the IoU between the prediction and the ground truth is larger than m) in the top-n predictions. Following previous methods , we

Implementation Details
Following (Zhang et al. 2020a), we utilize C3D (Tran et al. 2015) to extract visual features for the ActivityNet-Captions dataset and VGG (Simonyan and Zisserman 2014) for the Charades-STA dataset. N C and N V are set to 256 and 32, respectively, where the kernel size and stride of the 1D average pooling layer are both 8. The clip length r 1 and r 2 are 0.2 and 0.9 for the ActivityNet-Captions dataset while 0.2 and 0.5 for the Charades-STA dataset. The α factor is set as 0.25. The proposal threshold t h and t l are set as 0.7 and 0.5. Our model is trained on AdamW (Loshchilov and Hutter 2017) optimizer with the reduce-on-plateau learning rate decay strategy. The initial learning rate is set to 2e-4 and the training batch size is set to 64. The loss weights w B , w P , w R , w M are set as 0.4, 1.0, 1.0, 10.0.

Performance Comparisons
ActivityNet-Captions Dataset. We show the performance comparison on ActivityNet-Captions in Table 1. Our method almost outperforms all weakly supervised methods on "R@5", denoting a high recall for grounding. For "R@1", Our method obtained competitive performance compared to SCN and MARN.
Charades-STA Dataset. We further show the performance comparison on Charades-STA in Table 2. Our method outperforms other methods on "R@1, IoU=0.7" and "R@5, IoU=0.7", which means that our proposed method is able to predict more precise boundaries than other methods. For "R@5, IoU=0.3", there exists only a 0.02% gap between our method and the result of SCN. We also achieve competitive performance compared to SCN and MARN for "R@1, IoU=0.3" and "R@1, IoU=0.5".

Ablation Study
In this section, we design the ablation experiments on ActivityNet-Captions, as shown in Table 3: • Multi-queries Evaluation To evaluate the effect of the three queries (two relevant and one negative), we train the network on (1) QueryA only, (2) QueryA+QueryB, and (3) all the three queries. The results in Table 3 show that the model trained with the last configuration achieves better performance than the model trained with a single Query or without the negative query. We believe that training on two relevant queries can fully explore the composed video while the negative one can prevent the model from over-fitting the obvious boundary artifacts caused by composition.
• Training w/o Ratio Prediction To verify the effect of the composition ratio prediction, we remove the video-query alignment branch directly and keep other settings the same as the proposed method. The results in Table 3 show that it slightly hurts the performance when removing the ratio prediction module. However, learning holistic features by global reasoning tasks is meaningful for temporal grounding in those videos that do not hold obvious scene transitions. We leave this exploration in the future.
• Training w/o Masked Word Prediction To verify the effect of the masked word prediction, we feed the complete query sentences to the network and remove the masked word prediction module. Masked word prediction requires the linguistic features to consider their linguistic contexts and fully utilize the visual components to accurately predict the masked words, which help learn a compact and effective visual-linguistic representation. The results in Table 3 have supported this claim, where the performance drops by about 2% on all the metrics.

Qualitative Analysis
Further, we visualize several examples from Charades-STA and ActivityNet-Captions as shown in Figure 3. We can observe that there exists clear intra-contrast of (a) and (b) in Figure3 but also inter-contrast between (a) and (b). Since our method is designed for exploring such inter-contrast under a weakly-supervised setting, it can also localize the target segments well within the regular videos (a) and (b). In Figure3 (c), the intra-contrast is not so obvious, and our network also yields excellent predictions, which means that our method can also well tackle the videos with tiny intracontrast and capture the fine-grained semantic information inside the videos. In Figure3 (d), the whole video presents large-scale scene transitions, and the text query contains complex descriptions. Our network, however, still performs well in most cases except for the beginning prediction.

Conclusion
This paper proposes a simple yet effective video composition strategy for weakly supervised temporal sentence grounding. Specifically, we compose a video by sampling different clips from videos where inter-contrast is always present. Then, we tackle the weakly supervised temporal sentence grounding in a fully supervised manner. By imposing multi-tasks including proposal classification, moment classification, clip ratio prediction, and masked word prediction, the network explores the inter-contrast between videos within a composed video when given different queries. The promising results on the Charades-STA and the ActivityNet-Caption demonstrate the effectiveness of our proposed method. We hope that this research work could provide insight for temporal sentence grounding and other research fields such as pre-training.