← 研踪 PaperTrailCVF · CVPR 2026:cvf:0ae14addf2c32bbbfa72ec2bca140d54 · 记录 #1
MeToM: Metadata-Guided Token Merging for Efficient Video LLMs
来源日期（仅到年份） 2026
来源记录日期 2026
雷达观测 2026-11-01 23:27
摘要
Video Large Language Models (VLLMs) encounter significant computational challenges due to the large volume of visual tokens generated from multiple frames. Existing visual token pruning methods fail to account for the uneven spatiotemporal information density, thus squandering scarce token budgets on regions with low information density. In this paper, we propose a training-free Metadata-guided Token Merging framework (MeToM) that leverages intrinsic video metadata to adaptively allocate budgets and merge visual tokens based on content complexity. Specifically, MeToM exploits residual data from codec metadata as spatial information density cues. It merges less informative regions during tokenization, avoiding redundant encoding and improving the efficiency of the visual encoder. Additionally, MeToM captures temporal variations in information density by utilizing the average Group of Pictures (GoP) packet size to represent scene complexity. This mechanism enables dynamic per-frame token allocation across time, assigning more tokens to content-complex frames and fewer tokens to information-sparse ones. Finally, we merge low-contribution visual tokens via multi-layer attention to reduce prefill FLOPs and the visual KV-cache footprint inside the LLM. Extensive experimental results demonstrate that MeToM outperforms prior state-of-the-art methods. Notably, it achieves a 2.65xinference speedup over the baseline VLLM without sacrificing accuracy.
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记录 #1 · 2026 · 最新观测版本
MeToM: Metadata-Guided Token Merging for Efficient Video LLMs
Video Large Language Models (VLLMs) encounter significant computational challenges due to the large volume of visual tokens generated from multiple frames. Existing visual token pruning methods fail to account for the uneven spatiotemporal information density, thus squandering scarce token budgets on regions with low information density. In this paper, we propose a training-free Metadata-guided Token Merging framework (MeToM) that leverages intrinsic video metadata to adaptively allocate budgets and merge visual tokens based on content complexity. Specifically, MeToM exploits residual data from codec metadata as spatial information density cues. It merges less informative regions during tokenization, avoiding redundant encoding and improving the efficiency of the visual encoder. Additionally, MeToM captures temporal variations in information density by utilizing the average Group of Pictures (GoP) packet size to represent scene complexity. This mechanism enables dynamic per-frame token allocation across time, assigning more tokens to content-complex frames and fewer tokens to information-sparse ones. Finally, we merge low-contribution visual tokens via multi-layer attention to reduce prefill FLOPs and the visual KV-cache footprint inside the LLM. Extensive experimental results demonstrate that MeToM outperforms prior state-of-the-art methods. Notably, it achieves a 2.65xinference speedup over the baseline VLLM without sacrificing accuracy.
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