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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 tok
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