LLaVA-Scissor: Token Compression with Semantic Connected Components for Video LLMs

TL;DR

LLaVA-Scissor introduces a semantic connected components-based token compression method for video LLMs, effectively reducing tokens while maintaining semantic coverage and improving performance on various video understanding tasks.

Contribution

It proposes a novel, training-free token compression strategy using semantic connected components for better semantic coverage in video LLMs.

Findings

Outperforms existing token compression methods in benchmarks.

Maintains high performance at low token retention ratios.

Effective in diverse video understanding tasks.

Abstract

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.