
\paragraph{Limitations} Our method shows reasonable improvements over recent approaches leveraging instance embeddings only. However, producing interpretable maps that explicitly highlight pixel-level correspondence between images rather than attention scores may be more desirable. Moreover, it may be computationally prohibitive when larger backbones are employed with more heads. Thus, extension of our method to fully-convolutional MAB with spatial features may be interesting, and is left for future research.

\section{Conclusion}
\label{sec:conclusion}

In this work, we revisited discriminative self-attention for few-shot classification via cross-relational adaptation of support and queries. Our work has shown that attending to features in a single view only may be limited, instead cross-adaptation that attends to relevant features via re-weighting with task prototypes can boost performance and generalize better in the cross-domain setting. We also demonstrate state-of-the-art performance without the requirement of transfer learning.

\paragraph{Acknowledgments} This work was supported by the DGIST R\&D program of the Ministry of Science and ICT of KOREA (19-RT-01 and 21-DPIC-08), and IITP grant funded by the Korean government(MSIT) (No.2021-0-02068, Artificial Intelligence Innovation Hub).