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

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Christian Becker-Carus,Mike Wendteal surgical scenarios. Although a few works have explored single-image real-world desmoking in unpaired learning manners, they still encounter challenges in handling dense smoke. In this work, we address these issues together by introducing the self-supervised surgery video desmoking (SelfSVD). On

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https://doi.org/10.1007/978-3-642-75228-5aluate Chains against a new benchmark for complex multi-human scene synthesis, showing superior performance in human quality and scene accuracy over existing methods. Remarkably, Chains achieves this with?under 0.45?s for a 20-step inference, demonstrating?both effectiveness and efficiency.

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,Chains of?Diffusion Models,aluate Chains against a new benchmark for complex multi-human scene synthesis, showing superior performance in human quality and scene accuracy over existing methods. Remarkably, Chains achieves this with?under 0.45?s for a 20-step inference, demonstrating?both effectiveness and efficiency.

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Conference proceedings 2025nt learning; object recognition; image classification; image processing; object detection; semantic segmentation; human pose estimation; 3d reconstruction; stereo vision; computational photography; neural networks; image coding; image reconstruction; motion estimation..

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0302-9743 reconstruction; stereo vision; computational photography; neural networks; image coding; image reconstruction; motion estimation..978-3-031-73219-5978-3-031-73220-1Series ISSN 0302-9743 Series E-ISSN 1611-3349

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Ph. Broemser,E. Th. Brücke,H. Wintersteinons. Last, we employ a segmentation method to compare CLIP distances among the segmented components, ultimately selecting the most promising object from the sampled subset. Extensive experiments demonstrate that our approach outperforms recent SOTA T2I methods. Surprisingly, our results even rival those of human artists, such as . in Fig.?..

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Emotion, Motivation und Volition,er-free framework that leverages the . training history of a helper model to identify spurious samples, and . a group-balanced training set from which a robust model can be trained. We show that TAB improves worst-group performance without . group information or model selection, outperforming existing methods while maintaining overall accuracy.

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