\begin{figure*} %[ht!]
\begin{center}
\includegraphics[width=0.9\linewidth, height=7cm]{fig/fig2.pdf}
\end{center}
\caption{
\textbf{Overview of the proposed approach}. We employ episodic meta-training from scratch to learn discriminative co-adapted embedding for support and query images. Concretely, a shared backbone extracts globally pooled features $\mathbf{Z}$ and performs feature adaptation using a shared self-attention module by considering support embeddings $\mathbf{Z_s}$ as keys and value pairs along with query $\mathbf{Z_q}$ to compute attention scores that re-weight $\mathbf{Z_s}$ to produce a prototype $\mathbf{p}$ added to $\mathbf{Z_s}$. Finally, we obtain $\mathbf{\hat{Z}_s}$ after a projection via $\mathbf{f}^{o}_{\theta}$ (the same intuition is used for $\mathbf{Z_q}$ key/value pairs with $\mathbf{Z_s}$ as the query).
}
\vspace{-2mm}
\label{fig:overview}
\end{figure*}