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

anachronistic

十字架

Manish Asthana,Kapil Dev Gupta,Arvind Kumarssing potential spurious correlations in datasets, annotating concepts for images, and refining the annotations for improved robustness. We evaluate the proposed method on multiple datasets, and the results demonstrate its effectiveness in reducing model reliance on spurious correlations while preserving its interpretability.

牲畜栏

,: Long-Form Video Understanding with Large Language Model as Agent,es used on average. These results demonstrate superior effectiveness and efficiency of our method over the current state-of-the-art methods, highlighting the potential of agent-based approaches in advancing long-form video understanding.

overreach

DAUNT

Sunil B. Bhoi,Jayesh M. Dhodiyaion learning of the natural world—and introduce Nature Multi-View (NMV), a dataset of natural world imagery including >3 million ground-level and aerial image pairs for over 6,000 plant taxa across the ecologically diverse state of California. The NMV dataset and accompanying material are available at ..

FOLD

Conference proceedings 2025uter 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; reinforceme

PACK

,Ex2Eg-MAE: A Framework for Adaptation of Exocentric Video Masked Autoencoders for Egocentric Socialntly excels across diverse social role understanding tasks. It achieves state-of-the-art results in Ego4D’s . challenge (+0.7% mAP, +3.2% Accuracy). For the . challenge, it achieves competitive performance with the state-of-the-art (–0.7% mAP, +1.5% Accuracy) without supervised training on external

繁重

,SAVE: Protagonist Diversification with ,tructure ,gnostic ,ideo ,diting,xtual embedding to properly represent the motion in a source video. We also regulate the motion word to attend to proper motion-related areas by introducing a novel pseudo optical flow, efficiently computed from the pre-calculated attention maps. Finally, we decouple the motion from the appearance o

Hyperlipidemia

,Meta-optimized Angular Margin Contrastive Framework for Video-Language Representation Learning, training guided by a small amount of unbiased meta-data and augmented by video-text data generated by large vision-language model, we improve video-language representations and achieve superior performances on commonly used video question answering and text-video retrieval datasets.

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