visual-attribution-distillation-6f4fce97·1 events·first seen Aliases: Visual Attribution Distillation
Researchers introduce Visual Attribution Distillation (VAD), a counterfactual algorithm for multimodal knowledge distillation that isolates visually attributable corrections from teacher signals during on-policy training. VAD evaluates a privileged-view teacher with and without visual evidence to estimate a signed proxy for visual evidence direction, then reconstructs a student-anchored supervision target from the visually aligned component. Evaluated across six fine-grained visual benchmarks at 4B and 9B parameter scales, VAD outperforms direct privileged-view distillation and visual-advantage weighting baselines. The work addresses the source-mixing problem in multimodal distillation, where visual signals are entangled with linguistic priors and teacher-specific artifacts.