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- W4385804776 abstract "The goal of this work is to segment and name regions of images without access to pixel-level labels during training. To tackle this task, we construct segmenters by distilling the complementary strengths of two foundation models. The first, CLIP [26], exhibits the ability to assign names to image content but lacks an accessible representation of object structure. The second, DINO [5], captures the spatial extent of objects but has no knowledge of object names. Our method, termed NamedMask, begins by using CLIP to construct category-specific archives of images. These images are pseudo-labelled with a category-agnostic salient object detector bootstrapped from DINO, then refined by category-specific segmenters using the CLIP archive labels. Thanks to the high quality of the refined masks, we show that a standard segmentation architecture trained on these archives with appropriate data augmentation achieves impressive semantic segmentation abilities for both single-object and multi-object images. As a result, our proposed NamedMask performs favourably against a range of prior work on five benchmarks including the VOC2012, COCO and large-scale ImageNet-S datasets." @default.
- W4385804776 created "2023-08-15" @default.
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- W4385804776 date "2023-06-01" @default.
- W4385804776 modified "2023-10-16" @default.
- W4385804776 title "NamedMask: Distilling Segmenters from Complementary Foundation Models" @default.
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- W4385804776 doi "https://doi.org/10.1109/cvprw59228.2023.00524" @default.
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