Relevance equilibrium network for cross-domain few-shot learning

Cross-domain few-shot learning (CD-FSL) aims to develop a robust and generalizable model from a data-abundant source domain and apply it to the data-scarce target domain. An intrinsic challenge in CD-FSL is the domain shift problem, often manifested as a discrepancy in data distributions. This work addresses the domain shift problem from a model learning perspective, characterizing it in two specific aspects: over-sensitivity and excessive invariance. Specifically, we introduce a novel Relevance Equilibrium Network (ReqNet) to enhance the generalizability of few-shot models on target domain tasks. In particular, we design a Style Augmentation (StyleAug) module to diversify low-level visual styles of feature representations, alleviating the model's over-sensitivity to class- or task-irrelevant changes. Furthermore, to mitigate the excessive invariance to features relevant to the class and task, we devise a Task Context Modeling (TCM) module that strategically employs non-local operations to incorporate comprehensive task-level information. Extensive experiments and ablation studies are conducted on eight datasets to demonstrate the competitive performance of our proposed ReqNet.