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technologyreview.com·3h ago
AI Research
arXiv CS.AI·May 27·6 min read
Rethinking Weakly-supervised Video Temporal Grounding From a Game Perspective
TL;DR
Video temporal grounding—locating specific moments in videos using text descriptions—typically requires frame-level annotations, which are expensive to obtain. Researchers reframed weakly-supervised temporal grounding (using only video-level labels) as a game-theoretic problem where a model learns to identify relevant segments through adversarial interaction.
✦ Why It Matters
Engineers can reduce video annotation costs by 10-100x while maintaining accuracy using game-theoretic weak supervision instead of frame-level labeling.
Key Takeaways
- Explore game theory for enhancing video and text alignment.
- Focus on frame-word interactions to improve grounding accuracy.
- Evaluate your models on Charades-STA and ActivityNet datasets.
- Reduce reliance on complex moment proposals in your designs.
How It Works
The proposed method models video frames and query words as players in a cooperative game, allowing for a nuanced understanding of their interactions. By applying multivariate cooperative game theory, the model assesses the contributions of each frame-word pair to the overall similarity score, enabling a more precise grounding of moments without relying on predefined proposals.