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AdaVSkip is proposed, which equips each layer with two lightweight routers that independently determine whether visual tokens pass through by or skip the self-attention and MLP modules, and maintains strong task performance with substantially less computation.
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...
PreGress is proposed, the first ranking-native pre-training and prompting framework for supporting a wide range of node ranking tasks, and designs lightweight, task-specific prompt modules that adapt a frozen ranking backbone to downstream tasks without full retraining.
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