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DiffusionFeatures
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2026-02-13 06:32:39
raw text
DiffusionFeatures Emergent Correspondence from Image Diffusion Luming Tang * , Menglin Jia * , Qianqian Wang * , Cheng Perng Phoo , Bharath Hariharan Cornell University (*Equal contribution) NeurIPS 2023 arXiv Code Colab Without any supervision, Diffusion Features can find correspondences on real images across instances, categories, and even domains. Abstract Finding correspondences between images is a fundamental problem in computer vision. In this paper, we show that correspondence emerges in image diffusion models without any explicit supervision . We propose a simple strategy to extract this implicit knowledge out of diffusion networks as image features, namely DI ffusion F ea T ures ( DIFT ), and use them to establish correspondences between real images. Without any additional fine-tuning or supervision on the task-specific data or annotations, DIFT is able t...
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