  Most few-shot learning works rely on the same domain assumption between the
base and the target tasks, hindering their practical applications. This paper
proposes an adaptive transformer network (ADAPTER), a simple but effective
solution for cross-domain few-shot learning where there exist large domain
shifts between the base task and the target task. ADAPTER is built upon the
idea of bidirectional cross-attention to learn transferable features between
the two domains. The proposed architecture is trained with DINO to produce
diverse, and less biased features to avoid the supervision collapse problem.
Furthermore, the label smoothing approach is proposed to improve the
consistency and reliability of the predictions by also considering the
predicted labels of the close samples in the embedding space. The performance
of ADAPTER is rigorously evaluated in the BSCD-FSL benchmarks in which it
outperforms prior arts with significant margins.
