FOCUS: Efficient Keyframe Selection for Long Video Understanding

• URL: http://arxiv.org/abs/2510.27280v1
• Date: Fri, 31 Oct 2025 08:41:13 GMT
• Title: FOCUS: Efficient Keyframe Selection for Long Video Understanding
• Authors: Zirui Zhu, Hailun Xu, Yang Luo, Yong Liu, Kanchan Sarkar, Zhenheng Yang, Yang You,
• Abstract summary: Multimodal large language models (LMLMs) represent images and video frames as visual tokens.<n> FOCUS, Frame-Optimistic Confidence Upperbound Selection, is a model-agnostic selection module that selects frames under a strict token budget.<n>For videos longer than 20 minutes, it achieves an 11.9% gain in accuracy on LongVideoBenching benchmarks.
• Score: 26.44459739499484
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multimodal large language models (MLLMs) represent images and video frames as visual tokens. Scaling from single images to hour-long videos, however, inflates the token budget far beyond practical limits. Popular pipelines therefore either uniformly subsample or apply keyframe selection with retrieval-style scoring using smaller vision-language models. However, these keyframe selection methods still rely on pre-filtering before selection to reduce the inference cost and can miss the most informative moments. We propose FOCUS, Frame-Optimistic Confidence Upper-bound Selection, a training-free, model-agnostic keyframe selection module that selects query-relevant frames under a strict token budget. FOCUS formulates keyframe selection as a combinatorial pure-exploration (CPE) problem in multi-armed bandits: it treats short temporal clips as arms, and uses empirical means and Bernstein confidence radius to identify informative regions while preserving exploration of uncertain areas. The resulting two-stage exploration-exploitation procedure reduces from a sequential policy with theoretical guarantees, first identifying high-value temporal regions, then selecting top-scoring frames within each region On two long-video question-answering benchmarks, FOCUS delivers substantial accuracy improvements while processing less than 2% of video frames. For videos longer than 20 minutes, it achieves an 11.9% gain in accuracy on LongVideoBench, demonstrating its effectiveness as a keyframe selection method and providing a simple and general solution for scalable long-video understanding with MLLMs.

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