  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.
