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Project page for 'Deep ViT Features as Dense Visual Descriptors.'
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Deep ViT Features as Dense Visual Descriptors Deep ViT Features as Dense Visual Descriptors Shir Amir 1 Yossi Gandelsman 2 Shai Bagon 1 Tali Dekel 1 1 Weizmann Institute of Science 2 Berkeley Artificial Intelligence Research ECCVW 2022 "WIMF" Best Spotlight Presentation | Paper | Supplementary Material | Code | Abstract We leverage deep features extracted from a pre-trained Vision Transformer (ViT) as dense visual descriptors. We demonstrate that such features, when extracted from a self-supervised ViT model (DINO-ViT), exhibit several striking properties: (i) the features encode powerful high level information at high spatial resolution--i.e., capture semantic object parts at fine spatial granularity, and (ii) the encoded semantic information is shared across related, yet different object categories (i.e. super-categories). These properties allow us to design powerful dense ViT descriptors that facilitate a variety of applicat...
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