Cross-Domain Few-Shot Learning (CDFSL) endeavors to transfer generalized knowledge from the source domain to target domains using only a minimal amount of training data, which faces a triplet of learning challenges in the meantime, i.e., semantic disjoint, large domain discrepancy, and data scarcity. Different from predominant CDFSL works focused on generalized representations, we make novel attempts to construct Intermediate Domain Proxies (IDP) with source feature embeddings as the codebook and reconstruct the target domain feature with this learned codebook. We then conduct an empirical study to explore the intrinsic attributes from perspectives of visual styles and semantic contents in intermediate domain proxies. Reaping benefits from these attributes of intermediate domains, we develop a fast domain alignment method to use these proxies as learning guidance for target domain feature transformation. With the collaborative learning of intermediate domain reconstruction and target feature transformation, our proposed model is able to surpass the state-of-the-art models by a margin on 8 cross-domain few-shot learning benchmarks.

翻译：跨域小样本学习（CDFSL）旨在利用极少量的训练数据，将源域中的泛化知识迁移至目标域，同时面临三重学习挑战：语义分离、大域差异及数据稀缺。与当前主要关注泛化表征的CDFSL研究不同，本文创新性地尝试构建中间域代理（IDP），以源域特征嵌入作为码本，并利用该学习到的码本重构目标域特征。随后，我们通过实证研究从视觉风格和语义内容两个视角探究中间域代理的内在属性。受益于中间域的这些属性，我们开发了一种快速域对齐方法，将这些代理作为目标域特征变换的学习指导。通过中间域重构与目标特征变换的协同学习，我们提出的模型在8个跨域小样本学习基准测试中显著超越了现有最优模型。