
COCA introduces a novel approach using textual prototypes to enhance few-shot learners in the SF-UniDA scenario.


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coca-classifier-oriented-calibration-for-source-free-universal-domain-adaptation


COCA: Classifier-Oriented Calibration for Source-Free Universal Domain Adaptation



COCA method utilizes textual prototypes for few-shot learners in SF-UniDA, outperforming existing models.



COCA: Classifier-Oriented Calibration via Textual Prototype for Source-Free Universal Domain Adaptation
