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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https://doi.org/10.1007/978-3-642-69088-4nowledge about regions of interest, indicating a consistent user intention. To capture diverse user intentions, we further propose the click prompt optimal transport module (CPOT) to match click prompts and visual features. CPOT is designed to learn an optimal mapping between click prompts and visua

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Allergie und der respiratorische Infekt,uction for compensating high-quality spectral signal details. To our knowledge, this is the first approach to integrate physics-driven deep unfolding with generative LDM in the context of CASSI reconstruction. Comparisons on synthetic and real-world datasets illustrate the superiority of our propose

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https://doi.org/10.1007/978-3-662-58140-7tails that are often overlooked during downsampling, focusing on small but critical tampering clues. Rigorous testing on the DocTamper dataset confirms FFDN’s preeminence, significantly outperforming existing state-of-the-art methods in detecting tampering.

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https://doi.org/10.1007/978-3-642-91782-0information embedding module aggregates the latent context information into categorical features, and thus the label correlation can be directly established. Moreover, we use the label correlation capturing module to model label correlations with full and partial manners, respectively. Comprehensive

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C. B. Schmidt-Weber Prof. Dr. rer. nat.verse datasets and watermarking methods. As invisible watermarks become increasingly prevalent, while specific decoding techniques remain undisclosed, our approach provides a versatile solution and establishes a path toward increasing accountability, transparency, and trust in our digital visual con

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Genetik allergischer Erkrankungen,digm to progressively discover small objects and static-but-mobile objects that are missed by motion segmentation. As a result, though only learned from unlabeled videos, MOD-UV can detect and segment mobile objects from a single static image. Empirically, we achieve state-of-the-art performance in

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