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SD4Match SD4Match: Learning to Prompt Stable Diffusion Model for Semantic Matching CVPR 2024 Xinghui Li † , Jingyi Lu ‡ , Kai Han ‡ , Victor Adrian Prisacariu † † Active Vision Lab, University of Oxford ‡ Visual AI Lab, University of Hong Kong Arxiv Bibtex Code Abstract In this work, we address the challenge of matching semantically similar keypoints across image pairs. Existing research indicates that the intermediate output of the UNet within the Stable Diffusion (SD) framework can serve as robust image feature maps for such a matching task. We demonstrate that by employing a basic prompt tuning technique, the inherent potential of Stable Diffusion can be harnessed, resulting in a significant enhancement in accuracy over previous approaches. We further introduce a novel conditional prompting module that conditions the prompt on the local details of the input image pairs, leading to a further improvement in performance. We designat...
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