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

olfction

,Learning to?Drive via?Asymmetric Self-Play,data alone. The majority of driving data is uninteresting, and deliberately collecting?new long-tail scenarios is expensive and unsafe. We propose asymmetric self-play to scale beyond real?data with additional ., and . synthetic scenarios. Our approach pairs a teacher that learns to generate scenari

Control-Group

,OpenIns3D: Snap and?Lookup for?3D Open-Vocabulary Instance Segmentation,k-Snap-Lookup” scheme. The “Mask” module learns class-agnostic mask proposals in 3D point clouds, the “Snap” module generates synthetic scene-level images at multiple scales and leverages 2D vision-language models to extract interesting objects, and the “Lookup” module searches through the outcomes

誘導(dǎo)

FLAIL

Condense

Offensive

Open-Set Biometrics: Beyond Good Closed-Set Models,cations involve open-set biometrics, where probe subjects may or may not be present in the gallery. This poses distinct challenges in effectively distinguishing individuals in the gallery while minimizing false detections. While it is commonly believed that powerful biometric models can excel in bot

Limerick

比喻好

,Which Model Generated This Image? A Model-Agnostic Approach for?Origin Attribution,to identify the origin model?that generates them. In this work, we study the origin attribution?of generated images in a practical setting where only a few images generated by a source model are available and the source?model cannot be accessed. The goal is to check if a given image?is generated by

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Rinne-Test