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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Ordeal

Sanjay W. Pimplikar,Anupama Suryanarayanar 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

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Mathew A. Sherman,Sylvain E. Lesnétruggle to accurately estimate uncertainty when processing inputs drawn from the wild dataset. To address this issue, we introduce a novel instance-wise calibration method based on an energy model. Our method incorporates energy scores instead of softmax confidence scores, allowing for adaptive cons

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Alzheimer: 100 Years and Beyondth the proposed encoder layer and DyHead, a new dynamic TAD model, DyFADet, achieves promising performance on a series of challenging TAD benchmarks, including HACS-Segment, THUMOS14, ActivityNet-1.3, Epic-Kitchen?100, Ego4D-Moment QueriesV1.0, and FineAction. Code is released to ..

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,Teddy: Efficient Large-Scale Dataset Distillation via?Taylor-Approximated Matching,ents to a . one. On the other hand, rather than repeatedly training a novel model in each iteration, we unveil that employing a pre-cached pool of . models, which can be generated from a . base model, enhances both time efficiency and performance concurrently, particularly when dealing with large-sc

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,-VTON: Dynamic Semantics Disentangling for?Differential Diffusion Based Virtual Try-On,to handle multiple degradations independently, thereby minimizing learning ambiguities and achieving realistic results with minimal overhead. Extensive experiments demonstrate that .-VTON significantly outperforms existing methods in both quantitative metrics and qualitative evaluations, demonstrati