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Label Anything: An Interpretable, High-Fidelity and Prompt-Free Annotator

Fuente: arXiv

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Détails bibliographiques
Auteurs principaux:	Kou, Wei-Bin, Zhu, Guangxu, Ye, Rongguang, Wang, Shuai, Tang, Ming, Wu, Yik-Chung
Format:	Preprint
Publié:	2025
Sujets:	Robotics Machine Learning
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

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