K-frames: Scene-Driven Any-k Keyframe Selection for long video understanding

• URL: http://arxiv.org/abs/2510.13891v1
• Date: Tue, 14 Oct 2025 06:23:22 GMT
• Title: K-frames: Scene-Driven Any-k Keyframe Selection for long video understanding
• Authors: Yifeng Yao, Yike Yun, Jing Wang, Huishuai Zhang, Dongyan Zhao, Ke Tian, Zhihao Wang, Minghui Qiu, Tao Wang,
• Abstract summary: K-frames is a novel paradigm for scene-driven selection that preserves temporal continuity.<n>Instead of selecting individual frames, K-frames predicts semantically coherent, query-relevant clips.<n>K-frames provides an effective, interpretable, and plug-and-play solution for selection at various scales.
• Score: 38.06179287702453
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multimodal Large Language Models (MLLMs) have demonstrated significant capabilities in image understanding, but long-video are constrained by context windows and computational cost. Uniform frame sampling often leads to substantial information loss. Meanwhile existing keyframe selection methods such as text-frame retrieval or RL-based frame optimization typically yield sparse and temporally disjointed frames, overlooking scene continuity and lacking flexibility for multi-scale frame selection. To address these limitations, we introduce K-frames, a novel paradigm for scene-driven keyframe selection that preserves temporal continuity. Instead of selecting individual frames, K-frames predicts semantically coherent, query-relevant clips, which enables any-k keyframes selection to meet diverse user budgets. To achieve this approach, we first introduce PeakClips, a dataset of 200K video highlights conditioned by query. Building on this dataset, K-frames learns clip2frame selection using a three-stage progressive curriculum. It involves two Supervised Fine-Tuning stages for temporal grounding and key-clip perception, followed by a Reinforcement Learning stage that directly optimizes the scene-driven prediction policy for downstream task without further annotations. Extensive experiments on major long-video understanding benchmarks demonstrate that K-frames provides an effective, interpretable, and plug-and-play solution for keyframe selection at various scales. Our dataset and model will be available.

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