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CoDA: Instructive Chain-of-Domain Adaptation with Severity-Aware Visual Prompt Tuning
March 27, 2024, 4:45 a.m. | Ziyang Gong, Fuhao Li, Yupeng Deng, Deblina Bhattacharjee, Xiangwei Zhu, Zhenming Ji
cs.CV updates on arXiv.org arxiv.org
Abstract: Unsupervised Domain Adaptation (UDA) aims to adapt models from labeled source domains to unlabeled target domains. When adapting to adverse scenes, existing UDA methods fail to perform well due to the lack of instructions, leading their models to overlook discrepancies within all adverse scenes. To tackle this, we propose CoDA which instructs models to distinguish, focus, and learn from these discrepancies at scene and image levels. Specifically, CoDA consists of a Chain-of-Domain (CoD) strategy and …
arxiv cs.cv domain domain adaptation prompt prompt tuning type visual
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