Rethinking Weakly-supervised Video Temporal Grounding From a Game Perspective

• URL: http://arxiv.org/abs/2605.26441v1
• Date: Tue, 26 May 2026 01:54:17 GMT
• Title: Rethinking Weakly-supervised Video Temporal Grounding From a Game Perspective
• Abstract summary: This paper addresses the challenging task of weakly-supervised video temporal grounding.<n>We make the first attempt to tackle this task from a novel game perspective, which effectively learns the uncertain relationship between each vision-language pair.<n>Experiments show that our method achieves superior performance on both Charades-STA and ActivityNet Caption datasets.
• Score: 86.60594418472238
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper addresses the challenging task of weakly-supervised video temporal grounding. Existing approaches are generally based on the moment proposal selection framework that utilizes contrastive learning and reconstruction paradigm for scoring the pre-defined moment proposals. Although they have achieved significant progress, we argue that their current frameworks have overlooked two indispensable issues: 1) Coarse-grained cross-modal learning: previous methods solely capture the global video-level alignment with the query, failing to model the detailed consistency between video frames and query words for accurately grounding the moment boundaries. 2) Complex moment proposals: their performance severely relies on the quality of proposals, which are also time-consuming and complicated for selection. To this end, in this paper, we make the first attempt to tackle this task from a novel game perspective, which effectively learns the uncertain relationship between each vision-language pair with diverse granularity and flexible combination for multi-level cross-modal interaction.Specifically, we creatively model each video frame and query word as game players with multivariate cooperative game theory to learn their contribution to the cross-modal similarity score. By quantifying the trend of frame-word cooperation within a coalition via the game-theoretic interaction, we are able to value all uncertain but possible correspondence between frames and words. Finally, instead of using moment proposals, we utilize the learned query-guided frame-wise scores for better moment localization.Experiments show that our method achieves superior performance on both Charades-STA and ActivityNet Caption datasets.

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