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CODI: Improving Self-Supervised Vision Transformers with Cross Distillation
Self-supervised Vision Transformers learn strong image-level representations, but their patch-level representations can be unreliable for dense prediction. We study this mismatch and find that the class token concentrates global semantics on a small number of representative patches, while dense pred
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