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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,Blind Image Deconvolution by?Generative-Based Kernel Prior and?Initializer via?Latent Encoding,nd initializer, one can obtain a high-quality initialization of the blur kernel, and enable optimization within a compact latent kernel manifold. Such a framework results in an evident performance improvement over existing DIP-based BID methods. Extensive experiments on different datasets demonstrat

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AdvDiff: Generating Unrestricted Adversarial Examples Using Diffusion Models,ity, realistic adversarial examples by integrating gradients of the target classifier interpretably. Experimental results on MNIST and ImageNet datasets demonstrate that AdvDiff is effective in generating unrestricted adversarial examples, which outperforms state-of-the-art unrestricted adversarial

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,: Spuriousness Mitigation with?Minimal Human Annotations,r complicated training strategies, .?curates a smaller yet more feature-balanced data subset, fostering the development of spuriousness-robust models. Experimental validations across key benchmarks demonstrate that .?competes with or exceeds the performance of leading methods while significantly red