  Recent studies show that self-attentions behave like low-pass filters (as
opposed to convolutions) and enhancing their high-pass filtering capability
improves model performance. Contrary to this idea, we investigate existing
convolution-based models with spectral analysis and observe that improving the
low-pass filtering in convolution operations also leads to performance
improvement. To account for this observation, we hypothesize that utilizing
optimal token mixers that capture balanced representations of both high- and
low-frequency components can enhance the performance of models. We verify this
by decomposing visual features into the frequency domain and combining them in
a balanced manner. To handle this, we replace the balancing problem with a mask
filtering problem in the frequency domain. Then, we introduce a novel
token-mixer named SPAM and leverage it to derive a MetaFormer model termed as
SPANet. Experimental results show that the proposed method provides a way to
achieve this balance, and the balanced representations of both high- and
low-frequency components can improve the performance of models on multiple
computer vision tasks. Our code is available at
$\href{https://doranlyong.github.io/projects/spanet/}{\text{https://doranlyong.github.io/projects/spanet/}}$.
