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Titlebook: Computer Vision – ECCV 2024; 18th European Confer Aleš Leonardis,Elisa Ricci,Gül Varol Conference proceedings 2025 The Editor(s) (if applic

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Allgemeine BankbetriebswirtschaftSaD outperforms state-of-the-art diffusion model-based test-time methods. Moreover, TT-SaD beats training-time methods when testing on data that are inaccessible during training. To our knowledge, the study of stain adaptation in diffusion model during testing time is relatively unexplored.
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,Test-Time Stain Adaptation with Diffusion Models for Histopathology Image Classification,SaD outperforms state-of-the-art diffusion model-based test-time methods. Moreover, TT-SaD beats training-time methods when testing on data that are inaccessible during training. To our knowledge, the study of stain adaptation in diffusion model during testing time is relatively unexplored.
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https://doi.org/10.1007/978-3-8349-8934-5e restoration network to be a robust approximation of a proximal operator along a pre-defined optimization trajectory. We demonstrate the effectiveness and scalability of our approach on two 3D Cone-Beam CT datasets and outperform state-of-the-art methods in terms of PSNR. Code is available at ..
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https://doi.org/10.1007/978-3-8349-8934-5cifically, we design ego-to-agent, ego-to-map, and ego-to-BEV interaction mechanisms with hierarchical dynamic key objects attention to better model the interactions. The experiments on the nuScenes benchmark show that our approach outperforms state-of-the-art methods. Project page at ..
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