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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,Fristen. Termine. (§§186–1930.),ross the platforms, we also devise the platform-bridge prompts for efficient visual feature alignment. Extensive experiments demonstrate the superiority of the proposed method on all existing video ReID datasets and our proposed G2A-VReID dataset. The code and datasets are available at ..

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,FreeAugment: Data Augmentation Search Across All Degrees of?Freedom,hod. It efficiently learns the number of transformations and a probability distribution over their permutations, inherently refraining from redundant repetition while sampling. Our experiments demonstrate that this joint learning of all degrees of freedom significantly improves performance, achievin

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,GS-Pose: Category-Level Object Pose Estimation via?Geometric and?Semantic Correspondence,r synthetic rendered partial observations from synthetic object models. The learned knowledge from synthetic data generalizes to observations of unseen objects in the real scenes, without any fine-tuning. We demonstrate this with a rich evaluation on the NOCS, Wild6D and SUN RGB-D benchmarks, showin

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ArtVLM: Attribute Recognition Through Vision-Based Prefix Language Modeling,age-object-attribute relations to use towards attribute recognition. Specifically, for each attribute to be recognized on an image, we measure the visual-conditioned probability of generating a short sentence encoding the attribute’s relation to objects on the image. Unlike contrastive retrieval, wh

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