March 14, 2024, 4:46 a.m. | Song Tang, Wenxin Su, Mao Ye, Xiatian Zhu

cs.CV updates on

arXiv:2311.16510v3 Announce Type: replace
Abstract: Source-Free Domain Adaptation (SFDA) aims to adapt a source model for a target domain, with only access to unlabeled target training data and the source model pre-trained on a supervised source domain. Relying on pseudo labeling and/or auxiliary supervision, conventional methods are inevitably error-prone. To mitigate this limitation, in this work we for the first time explore the potentials of off-the-shelf vision-language (ViL) multimodal models (e.g.,CLIP) with rich whilst heterogeneous knowledge. We find that directly …

abstract adapt arxiv data domain domain adaptation error foundation foundation model free labeling multimodal supervision training training data type

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