Coverage-Driven Adaptive Keyframe Selection for Video Understanding
Learn to optimize video understanding with adaptive keyframe selection using large vision-language models, reducing computational overhead
- Apply coverage-driven adaptive keyframe selection to video frames using large vision-language models
- Configure the model to score frame-query relevance before inference
- Select keyframes based on relevance scores to reduce computational overhead
- Test the approach on various video understanding tasks, such as action recognition or object detection
- Compare the results with existing keyframe selection methods to evaluate performance
Computer vision engineers and researchers can benefit from this technique to improve video analysis efficiency, while data scientists can apply this method to various video understanding tasks
💡 Adaptive keyframe selection can significantly reduce computational overhead in video understanding tasks by selectively processing relevant frames
Optimize video understanding with adaptive keyframe selection using LVLMs! #computerVision #videoAnalysis
Key Takeaways
Learn to optimize video understanding with adaptive keyframe selection using large vision-language models, reducing computational overhead
Full Article
Abstract:
arXiv:2608.00714v1 Announce Type: cross Abstract: Recent advances in large vision-language models (LVLMs) have enabled long-video understanding and analysis. However, processing the large number of frames in a video incurs substantial computational overhead. Existing methods reduce LVLM inference costs by scoring frame-query relevance before inference and selecting keyframes accordingly. Nevertheless, the distribution of relevant frames varies across queries, and these methods often need to scor
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