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新方法利用视频编解码器数据实现高效VLLM令牌压缩

研究人员开发了一种新颖的、无需训练的视频大语言模型(VLLMs)令牌压缩方法,该方法利用了视频编解码器中的信息。该方法使用现有的编码数据(如运动矢量和残差)来指导压缩过程,从而降低了比较视觉特征的计算成本。该方法旨在通过使令牌压缩更准确、资源消耗更少来提高VLLMs的效率,在各种视频理解基准测试中表现出与FlashVID等现有方法相当或更优的性能。 AI

影响 该方法通过降低计算开销,可以显著提高视频理解模型的效率和可扩展性。

排序理由 arXiv上发表的研究论文,详细介绍了一种用于VLLMs的新方法。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新方法利用视频编解码器数据实现高效VLLM令牌压缩

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arXiv上发表的研究论文,详细介绍了一种用于VLLMs的新方法。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.CV TIER_1 English(EN) · Xihua Sheng, Ruijie Liu, Chang Wen Chen ·

    面向视频大语言模型的视频编解码器引导无训练令牌压缩

    arXiv:2610.12959v1 Announce Type: new Abstract: Video large language models (VLLMs) rely on long visual token sequences to understand complex temporal content, which makes visual token compression important for efficient inference. Existing training-free compression methods mainl…