PEEK: Picking Essential frames via Efficient Knowledge distillation
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 selects the most informative frames from a video, but existing methods remain computationally expensive. We introduce PEEK, an efficient dynamic frame sampling method that distills caption-conditioned frame relevance rankings from a stronger teacher model into a lightweight temporal model that operates only on visual content. On ActivityNet Captions and MSR-VTT, PEEK outperforms state-of-the-art methods across...
| Authors | Killian Steunou, Anas Filali Razzouki, Khalil Guetari et al. |
|---|---|
| Primary category | cs.CV |
| Published | 29 May 2026 |
| Last updated at source | 19 August 2026 |
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