ACPruner: Visual Token Pruning as Biased Attention Coverage Maximization in LVLMs
Large Vision-Language Models (LVLMs) face significant computational inefficiencies caused by the large number of visual tokens. Existing visual token pruning methods mainly focus on either retaining individually important tokens or selecting mutually diverse ones. In this work, we revisit visual token pruning from a coverage perspective and formulate it as a biased attention coverage maximization problem. The key idea is to select a compact token subset whose encoder-side outgoing attention can jointly cover the image while assigning higher coverage priority to more informative regions. From this perspective, we propose ACPruner, a training-free visual token pruning framework for efficien...
| Authors | Xu Li, Yuxuan Liang, Yi Zheng et al. |
|---|---|
| Primary category | cs.CV |
| Published | 28 September 2026 |
| Last updated at source | 28 September 2026 |
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