  Cross-domain few-shot classification (CD-FSC) aims to identify novel target
classes with a few samples, assuming that there exists a domain shift between
source and target domains. Existing state-of-the-art practices typically
pre-train on source domain and then finetune on the few-shot target data to
yield task-adaptive representations. Despite promising progress, these methods
are prone to overfitting the limited target distribution since data-scarcity
and ignore the transferable knowledge learned in the source domain. To
alleviate this problem, we propose a simple plug-and-play Adaptive Semantic
Consistency (ASC) framework, which improves cross-domain robustness by
preserving source transfer capability during the finetuning stage. Concretely,
we reuse the source images in the pretraining phase and design an adaptive
weight assignment strategy to highlight the samples similar to target domain,
aiming to aggregate informative target-related knowledge from source domain.
Subsequently, a semantic consistency regularization is applied to constrain the
consistency between the semantic features of the source images output by the
source model and target model. In this way, the proposed ASC enables explicit
transfer of source domain knowledge to prevent the model from overfitting the
target domain. Extensive experiments on multiple benchmarks demonstrate the
effectiveness of the proposed ASC, and ASC provides consistent improvements
over the baselines. The source code will be released.
