LLaVA-Scissor: Token Compression with Semantic Connected Components for
  Video LLMs
Authors: name: Boyuan Sun, name: Jiaxing Zhao, name: Xihan Wei, name: Qibin Hou

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.