GreedyPrune: Retenting Critical Visual Token Set for Large Vision Language Models

• URL: http://arxiv.org/abs/2506.13166v1
• Date: Mon, 16 Jun 2025 07:21:11 GMT
• Title: GreedyPrune: Retenting Critical Visual Token Set for Large Vision Language Models
• Authors: Ruiguang Pei, Weiqing Sun, Zhihui Fu, Jun Wang,
• Abstract summary: GreedyPrune is a training-free visual token pruning algorithm designed to optimize semantic saliency and visual diversity.<n>We show that GreedyPrune achieves state-of-the-art accuracy across various multimodal tasks and models while significantly reducing end-to-end inference latency.
• Score: 5.025353943896242
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Although Large Vision Language Models (LVLMs) have demonstrated remarkable performance in image understanding tasks, their computational efficiency remains a significant challenge, particularly on resource-constrained devices due to the high cost of processing large numbers of visual tokens. Recently, training-free visual token pruning methods have gained popularity as a low-cost solution to this issue. However, existing approaches suffer from two key limitations: semantic saliency-based strategies primarily focus on high cross-attention visual tokens, often neglecting visual diversity, whereas visual diversity-based methods risk inadvertently discarding semantically important tokens, especially under high compression ratios. In this paper, we introduce GreedyPrune, a training-free plug-and-play visual token pruning algorithm designed to jointly optimize semantic saliency and visual diversity. We formalize the token pruning process as a combinatorial optimization problem and demonstrate that greedy algorithms effectively balance computational efficiency with model accuracy. Extensive experiments validate the effectiveness of our approach, showing that GreedyPrune achieves state-of-the-art accuracy across various multimodal tasks and models while significantly reducing end-to-end inference latency.

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