\begin{table}[!h]
	\centering
	\scalebox{0.90}{
	\begin{tabular}{ccccc}
		\toprule
		$\text{MAB}^{S}_h$&$\text{MAB}^{Q}_h$&$\text{MAB}^{*}_h$& $1$-shot & $5$-shot \\
		\midrule
		\cmark&\xmark&\xmark &56.00$\pm$0.79 & 85.10$\pm$0.61 \\
		\xmark&\cmark&\xmark &76.33$\pm$0.73 & 80.10$\pm$0.74 \\
		\xmark&\xmark&\cmark &56.01$\pm$0.86 & 79.94$\pm$0.72 \\
		\midrule
		\cmark&\cmark&\xmark &\bf77.56$\pm$0.72 & \bf87.68$\pm$0.57 \\
		\bottomrule
	\end{tabular}
	}
	\caption{
		Evaluation on the contribution of attention modules in the proposed method with ResNet12 on \textit{mini}ImageNet. $\text{MAB}^{*}_h$ denotes using self-attention only on both query and support features i.e., $z^{\prime}_s = \text{MAB}_h(z_s,z_s,z_s)$ \& $z^{\prime}_q = \text{MAB}_h(z_q,z_q,z_q)$. $\text{MAB}^{S}_h$ implies support adaptation: $z^{\prime}_s = \text{MAB}_h(z_q,z_s,z_s)$, whereas $\text{MAB}^{Q}_h$ is query adaptation  $z^{\prime}_q = \text{MAB}_h(z_s,z_q,z_q)$, respectively.
	}
	\label{tab:effectmodules}
\end{table}