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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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

Proclaim

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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excrete

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 ..

panorama

,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

Instinctive

睨视

,-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