  The domain shift between the source and target domain is the main challenge
in Cross-Domain Few-Shot Learning (CD-FSL). However, the target domain is
absolutely unknown during the training on the source domain, which results in
lacking directed guidance for target tasks. We observe that since there are
similar backgrounds in target domains, it can apply self-labeled samples as
prior tasks to transfer knowledge onto target tasks. To this end, we propose a
task-expansion-decomposition framework for CD-FSL, called Self-Taught (ST)
approach, which alleviates the problem of non-target guidance by constructing
task-oriented metric spaces. Specifically, Weakly Supervised Object
Localization (WSOL) and self-supervised technologies are employed to enrich
task-oriented samples by exchanging and rotating the discriminative regions,
which generates a more abundant task set. Then these tasks are decomposed into
several tasks to finish the task of few-shot recognition and rotation
classification. It helps to transfer the source knowledge onto the target tasks
and focus on discriminative regions. We conduct extensive experiments under the
cross-domain setting including 8 target domains: CUB, Cars, Places, Plantae,
CropDieases, EuroSAT, ISIC, and ChestX. Experimental results demonstrate that
the proposed ST approach is applicable to various metric-based models, and
provides promising improvements in CD-FSL.
