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arXiv · 2508.06869

VSI: Visual Subtitle Integration for Keyframe Selection to enhance Long Video Understanding

Jianxiang He·Meisheng Hong·Jungang Li·Weiyu Guo·Xuming Hu·Hui Xiong
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Abstract

Multimodal large language models (MLLMs) demonstrate exceptional performance in vision-language tasks, yet their processing of long videos is constrained by input context length and high computational costs. Sparse frame sampling thus becomes a necessary preprocessing step, with sampled frame quality directly impacting downstream performance. Existing keyframe search algorithms achieve a balance between efficiency and sampled frame quality but heavily rely on the visual modality alone. This makes them difficult to adapt to text-related tasks and often leads to retrieval results deviating from core semantic content. To address this, we propose the VISUAL-SUBTITLE INTEGRATION (VSI), a multimodal keyframe retrieval framework. It employs a dual-branch collaborative retrieval approach combining Video Search and Subtitle Match to fuse complementary visual and textual information for precise localization. Experiments on LongVideoBench and VideoMME demonstrate that VSI achieves state-of-the-art accuracy in keyframe retrieval while delivering breakthrough performance in text-related tasks and exhibiting strong generalization across other tasks.

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Jianxiang He, Meisheng Hong, Jungang Li, Weiyu Guo, Xuming Hu, Hui Xiong. 2025-08-09. VSI: Visual Subtitle Integration for Keyframe Selection to enhance Long Video Understanding. https://arxiv.org/abs/2508.06869

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