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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https://doi.org/10.1007/978-3-319-58359-4systems, such smoothness is usually obtained via ., which explicitly enforces models to perform well on perturbed examples. In this work, we show the surprising effectiveness of instead regularizing the gradient w.r.t. model inputs .. Penalizing input . is commonly believed to be a much inferior app

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https://doi.org/10.1007/978-3-319-58359-4mages . corresponding pose information. Current sparse-view 3D inference methods typically rely on camera poses to geometrically aggregate information from input views, but are not robust in-the-wild when such information is unavailable/inaccurate. In contrast, UpFusion sidesteps this requirement by

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Decentralised Planning Proceduresm is of great importance in the privacy community where obfuscation of features has been proposed as a technique for privacy-preserving machine learning?(ML) inference. In this benchmark, we characterize different obfuscation techniques and design different attack models. We propose multiple reconst

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