CSES: Training-Free Semantic Keyframe Selector for Efficient Video Understanding
Researchers have introduced CSES, a training-free semantic keyframe selector designed to reduce computational overhead in large vision-language models (LVLMs) for long-video understanding. The method adaptively determines the number of frames to score and keyframes to select based on the prominence of the frame-query relevance profile, addressing limitations of existing methods that score hundreds or thousands of frames. CSES formulates keyframe selection as a coverage problem, jointly accounting for semantic relevance, temporal redundancy, and visual redundancy. The approach is detailed in a paper announced on arXiv (arXiv:2608.00714) as a cross-type announcement. The work aims to improve efficiency in video analysis tasks, potentially benefiting applications in art documentation and archival video processing.
Key facts
- CSES is a training-free semantic keyframe selector for LVLMs.
- It adaptively determines the number of frames to score and keyframes to select.
- It estimates the prominence of the frame-query relevance profile to guide active acquisition.
- Keyframe selection is formulated as a coverage problem.
- The method accounts for semantic relevance, temporal redundancy, and visual redundancy.
- The paper is available on arXiv with ID 2608.00714.
- The announcement type is cross.
- The approach reduces computational overhead in long-video understanding.
Entities
Institutions
- arXiv