LRCP: Low-Rank Compressibility Guided Visual Token Pruning for Efficient LVLMs

TL;DR

LRCP is a novel, training-free visual token pruning method for LVLMs that leverages low-rank structure to efficiently reduce tokens while maintaining high performance.

Contribution

The paper introduces LRCP, a low-rank compressibility based token pruning framework that outperforms existing methods without requiring additional training.

Findings

LRCP preserves 94.7% of image understanding with 88.9% token reduction.

LRCP maintains 97.8% of video understanding accuracy with 87.5% token reduction.

Visual token representations exhibit a stable low-rank structure across models and datasets.

Abstract

Large vision-language models (LVLMs) achieve strong multimodal understanding, but their inference cost grows rapidly with the number of visual tokens, especially for high-resolution images and long videos. Existing attention-based methods estimate token importance from attention scores, which may introduce positional bias, while representation-based methods reduce visual redundancy based on feature relations or reconstruction errors, overlooking the global structure of the visual token set. In this paper, we revisit visual token compression from the perspective of low-rank compressibility. Across models and datasets, we observe that visual token representations exhibit a pronounced low-rank structure, with a dominant subspace that remains stable even after a large fraction of tokens is randomly removed. Motivated by this finding, we propose LRCP, a training-free compression framework that first estimates the dominant low-rank subspace of visual tokens via PCA, and then scores each token by its projection residual onto this subspace, retaining tokens that are poorly explained by the low-rank background. Extensive experiments show that LRCP achieves superior results, preserving 94.7% of the original image-understanding performance with an 88.9% token reduction and 97.8% of the average video-understanding accuracy with an 87.5% token reduction.