ZoomV: Temporal Zoom-in for Efficient Long Video Understanding
Quick summary
arXiv:2504.01407v3 Announce Type: replace-cross Abstract: Long video understanding poses a fundamental challenge for large video-language models (LVLMs) due to the overwhelming number of frames and the risk of losing essential context through naive downsampling. Inspired by the way humans watch videos on mobile phones, constantly zooming in on frames of interest, we propose ZoomV, a query-aware temporal zoom-in framework designed for efficient and accurate long video understanding. Specifically, ZoomV operates in three stages: (1) Temporal interests grounding: guided by the query, ZoomV retrie
Key takeaways
- arXiv:2504.01407v3 Announce Type: replace-cross Abstract: Long video understanding poses a fundamental challenge for large video-language models (LVLMs) due to the overwhelming number of frames and the risk of losing essential context through naive downsampling.
- Inspired by the way humans watch videos on mobile phones, constantly zooming in on frames of interest, we propose ZoomV, a query-aware temporal zoom-in framework designed for efficient and accurate long video understanding.
- Specifically, ZoomV operates in three stages: (1) Temporal interests grounding: guided by the query, ZoomV retrie
Why it matters
The importance of “ZoomV: Temporal Zoom-in for Efficient Long Video Understanding” will be measured by what changes in practice. User behavior, access conditions, verifiable performance and responsible-use outcomes are the signals worth following.
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