Few-Shot Domain Adaptation via Prompt-Guided Multi-prototype Alignment Network

Unsupervised domain adaptation (UDA) aims to transfer knowledge from a labeled source domain to an unlabeled target domain. However, scarcity of labeled source domain samples in practical scenarios due to high annotation costs or difficulty in sample acquisition poses a challenge. Recent work has proposed Few-Shot Unsupervised Domain Adaptation (FUDA) to address this challenge, achieving promising results through cross-domain self-supervised learning. However, their performance still significantly falls below that of UDA methods utilizing ample labeled source domain samples, primarily due to the scarcity of supervision, which hampers the model's ability to learn feature representations and classifiers that generalize well in the target domain. To address this issue, we introduce the visual-language pre-trained model CLIP as the backbone network and propose a Prompt-Guided Multi-Prototype Alignment framework (PMPA) for FUDA. By learning soft prompts containing diverse semantic information and aligning both source and target domains to multiple sets of class prototypes, PMPA achieves higher performance on the target domain with fewer source domain samples. Extensive experiments on OfficeHome, VisDA-2017, and Mini-DomainNet datasets demonstrate that our approach significantly outperforms previous state-of-the-art FUDA methods and achieves comparable performance to UDA methods utilizing ample source domain samples.