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title
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2026-02-26 22:39:14
raw text
RepurposeGANs Repurposing GANs for One-shot Semantic Part Segmentation Pitchaporn Rewatbowornwong* Nontawat Tritrong* Supasorn Suwajanakorn VISTEC - Vidyasirimedhi Institute of Science and Technology₁ Rayong, Thailand CVPR 2021 (Oral) *Equal contribution Segmentation results trained with only one label Abstract While GANs have shown success in realistic image generation, the idea of using GANs for other tasks unrelated to synthesis is underexplored. Do GANs learn meaningful structural parts of objects during their attempt to reproduce those objects? In this work, we test this hypothesis and propose a simple and effective approach based on GANs for semantic part segmentation that requires as few as one label example along with an unlabeled dataset. Our key idea is to leverage a trained GAN to extract pixel-wise ...
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