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PEEK: Picking Essential frames via Efficient Knowledge distillation
arXiv:2605.31029v1 Announce Type: new Abstract: Video-language models can process only a limited number of frames, making frame selection a key bottleneck for efficient video captioning. Most captioning pipelines still rely on uniform sampling, which is computationally cheap but agnostic to visual content. Adaptive frame sampling has recently emerged as a promising approach for selecting the most informative frames from a video; however, existing methods remain computationally expensive.