Papers › Negative Sample Matters: A Renaissance of Metric Learning for Temporal Grounding
Negative Sample Matters: A Renaissance of Metric Learning for Temporal Grounding
Zhenzhi Wang, LiMin Wang, Tao Wu, TianHao Li, Gangshan Wu
Temporal grounding aims to localize a video moment which is semantically aligned with a given natural language query. Existing methods typically apply a detection or regression pipeline on the fused representation with the research focus on designing complicated prediction heads or fusion strategies. Instead, from a perspective on temporal grounding as a metric-learning problem, we present a Mutual Matching Network (MMN), to directly model the similarity between language queries and video moments in a joint embedding space. This new metric-learning framework enables fully exploiting negative samples from two new aspects: constructing negative cross-modal pairs in a mutual matching scheme and mining negative pairs across different videos. These new negative samples could enhance the joint representation learning of two modalities via cross-modal mutual matching to maximize their mutual information. Experiments show that our MMN achieves highly competitive performance compared with the state-of-the-art methods on four video grounding benchmarks. Based on MMN, we present a winner solution for the HC-STVG challenge of the 3rd PIC workshop. This suggests that metric learning is still a promising method for temporal grounding via capturing the essential cross-modal correlation in a joint embedding space. Code is available at https://github.com/MCG-NJU/MMN.
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Temporal Sentence Grounding | Charades-STA | MMN (Full, MViT-K400-Pretrain-feature, evaluated by AdaFocus) | R1@0.5 | 55.2 | #4 of 13 | Archive leaderboard | report |
| Temporal Sentence Grounding | Charades-STA | MMN (Full, MViT-K400-Pretrain-feature, evaluated by AdaFocus) | R1@0.7 | 32.2 | #4 of 13 | Archive leaderboard | report |
| Temporal Sentence Grounding | Charades-STA | MMN (Full, MViT-K400-Pretrain-feature, evaluated by AdaFocus) | R5@0.5 | 88.3 | #4 of 13 | Archive leaderboard | report |
| Temporal Sentence Grounding | Charades-STA | MMN (Full, MViT-K400-Pretrain-feature, evaluated by AdaFocus) | R5@0.7 | 62.7 | #4 of 13 | Archive leaderboard | report |
| Temporal Sentence Grounding | Charades-STA | MMN (Full, I3D-K400-Pretrain-feature, evaluated by AdaFocus) | R1@0.5 | 49.4 | #5 of 13 | Archive leaderboard | report |
| Temporal Sentence Grounding | Charades-STA | MMN (Full, I3D-K400-Pretrain-feature, evaluated by AdaFocus) | R1@0.7 | 29.8 | #5 of 13 | Archive leaderboard | report |
| Temporal Sentence Grounding | Charades-STA | MMN (Full, I3D-K400-Pretrain-feature, evaluated by AdaFocus) | R5@0.5 | 85.8 | #5 of 13 | Archive leaderboard | report |
| Temporal Sentence Grounding | Charades-STA | MMN (Full, I3D-K400-Pretrain-feature, evaluated by AdaFocus) | R5@0.7 | 60.5 | #5 of 13 | Archive leaderboard | report |
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