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.

提出了一种自适应变压器网络 (ADAPTER) 作为跨领域少样本学习的简单而有效的解决方案，它建立在双向交叉注意力的思想上，学习两个领域之间的可转移特征，并通过 DINO 训练以避免监督崩溃问题。进一步提出了标签平滑方法来提高嵌入空间中近邻样本的预测一致性和可靠性。在 BSCD-FSL 基准测试中，ADAPTER 的性能得到了严格评估，表现优于以往的方法。