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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Bauliche Voraussetzungen und Hygienees. To address this, we introduce the concept of a meta-calibrator that performs uncertainty calibration for NeRFs with a single forward pass without the need for holding out any images from the target scene. Our meta-calibrator is a neural network that takes as input the NeRF images and uncalibrate

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W. Steggemann,C. Krabbe-Steggemanntrates consistent and substantial performance improvements over five popular benchmarks compared with state-of-the-art methods. Notably, on the CityScapes dataset, MetaAT achieves a 1.36% error rate in performance estimation using only 0.07% of annotations, marking a . improvement over existing stat

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,SeA: Semantic Adversarial Augmentation for Last Layer Features from Unsupervised Representation Lea on 11 benchmark downstream classification tasks with 4 popular pre-trained models. Our method is . better than the deep features without SeA on average. Moreover, compared to the expensive fine-tuning that is expected to give good performance, SeA shows a comparable performance on 6 out of 11 tasks

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,Unlocking the Potential of Federated Learning: The Symphony of Dataset Distillation via Deep Generaly minimizing resource utilization. We substantiate our claim with a theoretical analysis, demonstrating the asymptotic resemblance of the process to the hypothetical ideal of completely centralized training on a heterogeneous dataset. Empirical evidence from our comprehensive experiments indicates

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,Rethinking Fast Adversarial Training: A Splitting Technique to Overcome Catastrophic Overfitting,pagation, presenting an efficient solution to enhance adversarial robustness. Our comprehensive evaluation conducted across standard datasets, demonstrates that our DR splitting-based model not only improves adversarial robustness but also achieves this with remarkable efficiency compared to various

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,3D Hand Pose Estimation in Everyday Egocentric Images,, a system for 3D hand pose estimation in everyday egocentric images. Zero-shot evaluation on 4 diverse datasets (H2O, AssemblyHands, Epic-Kitchens, Ego-Exo4D) demonstrate the effectiveness of our approach across 2D and 3D metrics, where we beat past methods by 7.4% – 66%. In system level comparison