Unsupervised domain adaptation aims to transfer knowledge from a fully-labeled source domain to an unlabeled target domain. However, in real-world scenarios, providing abundant labeled data even in the source domain can be infeasible due to the difficulty and high expense of annotation. To address this issue, recent works consider the Few-shot Unsupervised Domain Adaptation (FUDA) where only a few source samples are labeled, and conduct knowledge transfer via self-supervised learning methods. Yet existing methods generally overlook that the sparse label setting hinders learning reliable source knowledge for transfer. Additionally, the learning difficulty difference in target samples is different but ignored, leaving hard target samples poorly classified. To tackle both deficiencies, in this paper, we propose a novel Confidence-based Visual Dispersal Transfer learning method (C-VisDiT) for FUDA. Specifically, C-VisDiT consists of a cross-domain visual dispersal strategy that transfers only high-confidence source knowledge for model adaptation and an intra-domain visual dispersal strategy that guides the learning of hard target samples with easy ones. We conduct extensive experiments on Office-31, Office-Home, VisDA-C, and DomainNet benchmark datasets and the results demonstrate that the proposed C-VisDiT significantly outperforms state-of-the-art FUDA methods. Our code is available at https://github.com/Bostoncake/C-VisDiT.

翻译：无监督域适应旨在将知识从完全标注的源域迁移至无标注的目标域。然而，在实际场景中，由于标注难度大且成本高昂，即便在源域提供充足标注数据也往往难以实现。为解决此问题，近期研究提出了少样本无监督域适应（FUDA）方法，仅对少量源样本进行标注，并通过自监督学习方法实现知识迁移。但现有方法普遍忽略了一个问题：稀疏标注的设置阻碍了可靠源知识的获取与迁移。此外，目标样本的学习难度存在差异却未被充分利用，导致困难目标样本分类效果不佳。针对这两点不足，本文提出了一种新颖的基于置信度的视觉分散式迁移学习方法（C-VisDiT）用于FUDA。具体而言，C-VisDiT包含两个核心策略：跨域视觉分散策略——仅将高置信度的源知识迁移至模型适应过程；域内视觉分散策略——利用简单目标样本引导困难目标样本的学习。我们在Office-31、Office-Home、VisDA-C和DomainNet基准数据集上进行了大量实验，结果表明所提出的C-VisDiT方法显著优于当前最先进的FUDA方法。我们的代码发布于https://github.com/Bostoncake/C-VisDiT。