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到未标注的目标域。然而在现实场景中，由于标注难度高且成本昂贵，即使在源域也难以提供充足的标注数据。为解决该问题，近期研究考虑少样本无监督域适应（FUDA），其中仅标注少量源域样本，并通过自监督学习方法实现知识迁移。现有方法普遍忽略了稀疏标注设置会阻碍学习可靠源域知识用于迁移这一事实。此外，目标域样本的学习难度存在差异但未被关注，导致困难样本分类效果不佳。针对上述两个缺陷，本文提出一种基于置信度的视觉分散迁移学习方法（C-VisDiT）用于FUDA。具体而言，C-VisDiT包含跨域视觉分散策略——仅转移高置信度源域知识进行模型适配，以及域内视觉分散策略——利用简单样本指导困难样本的学习。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显著优于当前最先进的FUDA方法。我们的代码开源在https://github.com/Bostoncake/C-VisDiT。