Titlebook: Computer Vision – ECCV 2024; 18th European Confer Ale? Leonardis,Elisa Ricci,Gül Varol Conference proceedings 2025 The Editor(s) (if applic

eczema

,Overcome Modal Bias in?Multi-modal Federated Learning via?Balanced Modality Selection,isting client selection methods simply consider the mining of distributed uni-modal data, yet, their effectiveness may diminish in multi-modal FL (MFL) as the modality imbalance problem not only impedes the collaborative local training but also leads to a severe global modality-level bias. We empiri

NIP

,Comprehensive Attribution: Inherently Explainable Vision Model with?Feature Detector,ribution method aims to enhance the understanding of model behavior by identifying the important regions in images that significantly contribute to predictions. It is achieved by cooperatively training a selector (generating an attribution map to identify important features) and a predictor (making

強(qiáng)制性

展覽

acrophobia

,Pre-trained Visual Dynamics Representations for?Efficient Policy Learning,ilable and inhere a vast amount of prior world knowledge, the absence of action annotations and the common domain gap with downstream tasks hinder utilizing videos for RL pre-training. To address the challenge of ., we propose .re-trained .isual .ynamics .epresentations (PVDR) to bridge the domain g

ANN

枕墊

名義上

,Follow the?Rules: Reasoning for?Video Anomaly Detection with?Large Language Models,little rationale behind detection, hindering public trust in real-world deployments. In this paper, we approach VAD with a reasoning framework. Although Large Language Models (LLMs) have shown revolutionary reasoning ability, we find that their direct use falls short of VAD. Specifically, the implic

TOXIC

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0302-9743 ce on Computer Vision, ECCV 2024, held in Milan, Italy, during September 29–October 4, 2024...The 2387 papers presented in these proceedings were carefully reviewed and selected from a total of 8585 submissions. They deal with topics such as computer vision; machine learning; deep neural networks; r