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

Fuente: arXiv

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Dettagli Bibliografici
Autori principali:	Kou, Wei-Bin, Zhu, Guangxu, Ye, Rongguang, Wang, Shuai, Tang, Ming, Wu, Yik-Chung
Natura:	Preprint
Pubblicazione:	2025
Soggetti:	Robotics Machine Learning
Accesso online:	Acceder al recurso
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