@inproceedings{huang-etal-2025-prunevid,
    title = "{P}rune{V}id: Visual Token Pruning for Efficient Video Large Language Models",
    author = "Huang, Xiaohu  and
      Zhou, Hao  and
      Han, Kai",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/display_plenaries/2025.findings-acl.1024/",
    pages = "19959--19973",
    ISBN = "979-8-89176-256-5",
    abstract = "We introduce PruneVid, a training-free visual token pruning method designed to enhance the efficiency of multimodal video understanding. While Large Language Models (LLMs) have shown promising performance on video tasks due to their advanced visual comprehension capabilities, the substantial redundancy inherent in video data poses significant computational challenges. To address this issue, PruneVid (1) reduces intrinsic video redundancy by merging temporally static and spatially similar tokens, and (2) leverages LLMs' inherent ability to selectively prune visual tokens irrelevant to specific queries, thereby improving model efficiency. We validate our method across multiple video benchmarks, demonstrating that PruneVid can prune over 80{\%} of tokens while maintaining competitive performance when combined with different video LLMs. Our results highlight PruneVid{'}s superior effectiveness and efficiency compared to existing pruning methods."
}