VLM-Pruner: Buffering for Spatial Sparsity in an Efficient VLM Centrifugal Token Pruning Paradigm

摘要

Vision-language models (VLMs) excel at image understanding tasks, but the large number of visual tokens imposes significant computational costs, hindering deployment on mobile devices. Many pruning methods rely solely on token importance and thus overlook inter-token redundancy, retaining numerous duplicated tokens and wasting capacity. Although some redundancy-aware approaches have been proposed, they often ignore the spatial relationships among visual tokens. This can lead to overly sparse selections of retained tokens that fail to adequately cover the regions of target objects. To address these limitations, we propose VLM-Pruner, a training-free token pruning algorithm that explicitly balances redundancy and spatial sparsity. Specifically, the centrifugal token pruning paradigm is introduced for near-to-far selection. Moreover, the Buffering for Spatial Sparsity (BSS) criterion delays the selection of spatially distant tokens, thereby prioritizing the preservation of fine-grained object details. To further improve efficiency, a parallel greedy strategy is adopted for token selection. To mitigate information loss from pruning, we selectively fuse salient information from the discarded tokens into the retained ones. Comprehensive comparisons demonstrate that VLM-Pruner consistently outperforms other competitive methods across five VLMs under various pruning ratios, while delivering an end-to-end inference speedup. The code is available at https://github.com/Casey-bit/VLMPruner.