Not All Attention Heads Contribute to Critical Visual Token Selection: Head-Aware Pruning Matters More
ProViP is proposed, a training-free progressive visual token pruning framework that removes redundant visual tokens based on the embedding similarity of input tokens before reasoning of the LLM backbone, and then prunes tokens during reasoning via head-aware pruning.
Chaofang Ma, Lin Jiang, Carol Jingyi Li et al.
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