FlexSelect: Flexible Token Selection for Efficient Long Video Understanding

Long-form video understanding poses a significant challenge for video large language models (VideoLLMs) due to prohibitively high computational and memory demands. In this paper, We propose $\textbf{FlexSelect}$, a flexible and efficient token selection strategy for processing long videos. FlexSelect identifies and retains the most semantically relevant content by leveraging cross-modal attention patterns from a reference transformer layer. It comprises two key components: (1) $\textbf{a training-free token ranking pipeline}$ that leverages faithful cross-modal attention weights to estimate each video token’s importance, and (2) $\textbf{a rank-supervised lightweight selector}$ that is trained to replicate these rankings and filter redundant tokens. This generic approach can be seamlessly integrated into various VideoLLM architectures, such as LLaVA-Video, InternVL and Qwen-VL, serving as a plug-and-play module to extend their temporal context length. Empirically, FlexSelect delivers strong gains across multiple long-video benchmarks – including VideoMME, MLVU, LongVB, and LVBench. Morever, it achieves significant speed-ups ($\textit{e. g. ,}$ up to 9 $\times$ on a LLaVA-Video-7B model), highlighting FlexSelect’s promise for efficient long-form video understanding. Project page: https: //flexselect. github. io

Authors

• yunzhu zhang
• Yu Lu Westlake University
• Tianyi Wang Zhejiang University
• Fengyun Rao WeChat, Tencent Inc.
• Yi Yang
• Linchao Zhu College of Computer Science and Technology, Zhejiang University

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