Learning to Predict Middle-Layer Attention in MLLMs for Visual Token Pruning
Middle-layer Attention Prediction (MAP), which uses Question Contrastive Teacher Selection to identify a sample-specific teacher layer by contrasting attention under the original and reference questions, and distills attention from the selected layer into a lightweight predictor that estimates visual token importance f...
Yu-Yao Sun, Tao Deng, Shuang-Hua Li et al.
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