Few-Shot transfer learning has become a major focus of research as it allows recognition of new classes with limited labeled data. While it is assumed that train and test data have the same data distribution, this is often not the case in real-world applications. This leads to decreased model transfer effects when the new class distribution differs significantly from the learned classes. Research into Cross-Domain Few-Shot (CDFS) has emerged to address this issue, forming a more challenging and realistic setting. In this survey, we provide a detailed taxonomy of CDFS from the problem setting and corresponding solutions view. We summarise the existing CDFS network architectures and discuss the solution ideas for each direction the taxonomy indicates. Furthermore, we introduce various CDFS downstream applications and outline classification, detection, and segmentation benchmarks and corresponding standards for evaluation. We also discuss the challenges of CDFS research and explore potential directions for future investigation. Through this review, we aim to provide comprehensive guidance on CDFS research, enabling researchers to gain insight into the state-of-the-art while allowing them to build upon existing solutions to develop their own CDFS models.

翻译：少样本迁移学习因允许利用有限标注数据识别新类别而成为研究焦点。尽管通常假设训练数据与测试数据具有相同的数据分布，但在实际应用中往往并非如此。当新类别分布与已学习类别存在显著差异时，模型迁移效果会下降。针对该问题，跨域少样本学习（CDFS）研究应运而生，构成了更具挑战性的现实场景。本综述从问题设定与对应解决方案视角，对CDFS进行了详细的分类体系构建。我们归纳了现有CDFS网络架构，并讨论了分类体系所指示各方向对应的解决方案思路。此外，我们介绍了多种CDFS下游应用，概述了分类、检测和分割任务的基准测试及相应评价标准。同时探讨了CDFS研究面临的挑战，并展望了未来潜在的研究方向。通过本综述，我们旨在为CDFS研究提供全面指导，使研究者能够洞察当前技术发展水平，并在现有解决方案基础上开发自身的CDFS模型。