LLaVA-Scissor: Token Compression with Semantic Connected Components for
  Video LLMs
In this paper, we present LLaVA-Scissor, a training-free token compression
strategy designed for video multimodal large language models. Previous methods
mostly attempt to compress tokens based on attention scores, but fail to
effectively capture all semantic regions and often lead to token redundancy.
Differently, we propose to leverage the Semantic Connected Components (SCC)
approach that assigns tokens to distinct semantic regions within the token set,
ensuring comprehensive semantic coverage. The outcome is a two-step
spatio-temporal token compression strategy that utilizes SCC in both spatial
and temporal domains. This strategy can effectively compress tokens by
representing the entire video with a set of non-overlapping semantic tokens. We
conduct extensive evaluations of the token compression capabilities of
LLaVA-Scissor across diverse video understanding benchmarks, including video
question answering, long video understanding, and comprehensive multi-choices
benchmarks. Experimental results show that the proposed LLaVA-Scissor
outperforms other token compression methods, achieving superior performance in
various video understanding benchmarks, particularly at low token retention
ratios. Project page: https://github.com/HumanMLLM/LLaVA-Scissor.
6861eea79e7509383d29ab2f
Boyuan Sun
claimed_verified
2025-07-01T10:14:32.392Z
66ef2611fcc1c455f8dce832
/avatars/c73ef2dfcd1e6ec8414a31226ad38e3b.svg
Boyuan Sun
user
BBBBCHAN
6861eea79e7509383d29ab30
Jiaxing Zhao
6861eea79e7509383d29ab31
Xihan Wei
6861eea79e7509383d29ab32
Qibin Hou
LLaVA-Scissor: Token Compression with Semantic Connected Components for
  Video LLMs
In this paper, we present LLaVA-Scissor, a training-free token compression
strategy designed for video multimodal large language models. Previous methods
mostly attempt to compress tokens based on attention scores, but fail to
effectively capture all semantic regions and often lead to token redundancy.
Differently, we propose to leverage the Semantic Connected Components (SCC)
approach that assigns tokens to distinct semantic regions within the token set,
ensuring comprehensive semantic coverage. The outcome is a two-step
spatio-temporal token compression strategy that utilizes SCC in both spatial
and temporal domains. This strategy can effectively compress tokens by
representing the entire video with a set of non-overlapping semantic tokens. We
conduct extensive evaluations of the token compression capabilities of
LLaVA-Scissor across diverse video understanding benchmarks, including video
question answering, long video understanding, and comprehensive multi-choices
benchmarks. Experimental results show that the proposed LLaVA-Scissor
outperforms other token compression methods, achieving superior performance in
various video understanding benchmarks, particularly at low token retention
ratios. Project page: https://github.com/HumanMLLM/LLaVA-Scissor.
LLaVA-Scissor, a token compression strategy for video multimodal large language models, uses Semantic Connected Components to compress tokens effectively while maintaining semantic coverage and outperforming other methods.
token compression strategy Semantic Connected Components (SCC) spatio-temporal token compression strategy video question answering long video understanding comprehensive multi-choice benchmarks