June 5, 2024, 4:48 a.m. | Song Tang, Wenxin Su, Mao Ye, Jianwei Zhang, Xiatian Zhu

cs.CV updates on arXiv.org arxiv.org

arXiv:2406.01658v1 Announce Type: new
Abstract: Source-free Domain Adaptation (SFDA) aims to adapt a pre-trained source model to an unlabeled target domain with no access to the source data. Inspired by the success of pre-trained large vision-language (ViL) models in many other applications, the latest SFDA methods have also validated the benefit of ViL models by leveraging their predictions as pseudo supervision. However, we observe that ViL's predictions could be noisy and inaccurate at an unknown rate, potentially introducing additional negative …

abstract access adapt applications arxiv benefit cs.cv data denoising domain domain adaptation free language latest source data success type vision vision-language

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