SparseVLM: Visual Token Sparsification for Efficient Vision Language Models Inference

ICLR 2025 Conference Submission402 Authors

Keywords: Sparsification, Vision Language Model, Efficiency

TL;DR: We propose an efficient text-aware training-free vision token optimization mechanism called SparseVLM.

Abstract: In vision-language models (VLMs), visual tokens usually consume a significant amount of computational overhead, despite their sparser information density compared to text tokens. To address this, most existing methods learn a network to prune redundant visual tokens and require additional training data. Differently, we propose an efficient training-free token optimization mechanism dubbed SparseVLM without extra parameters or fine-tuning costs. Concretely, given that visual tokens complement text tokens in VLMs for linguistic reasoning, we select visual-relevant text tokens to rate the significance of vision tokens within the self-attention matrix extracted from the VLMs. Then we progressively prune irrelevant tokens. To maximize sparsity while retaining essential information, we introduce a rank-based strategy to adaptively determine the sparsification ratio for each layer, alongside a token recycling method that compresses pruned tokens into more compact representations. Experimental results show that our SparseVLM improves the efficiency of various VLMs across a range of image and video understanding tasks. In particular, LLaVA equipped with SparseVLM reduces 61\% $\sim$ 67\% FLOPs with a compression ratio of 78\% while maintaining 93\% of the accuracy.

Primary Area: applications to computer vision, audio, language, and other modalities

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Submission Number: 402