  Recently, MLP-based vision backbones emerge. MLP-based vision architectures
with less inductive bias achieve competitive performance in image recognition
compared with CNNs and vision Transformers. Among them, spatial-shift MLP
(S$^2$-MLP), adopting the straightforward spatial-shift operation, achieves
better performance than the pioneering works including MLP-mixer and ResMLP.
More recently, using smaller patches with a pyramid structure, Vision
Permutator (ViP) and Global Filter Network (GFNet) achieve better performance
than S$^2$-MLP.
  In this paper, we improve the S$^2$-MLP vision backbone. We expand the
feature map along the channel dimension and split the expanded feature map into
several parts. We conduct different spatial-shift operations on split parts.
  Meanwhile, we exploit the split-attention operation to fuse these split
parts. Moreover, like the counterparts, we adopt smaller-scale patches and use
a pyramid structure for boosting the image recognition accuracy. We term the
improved spatial-shift MLP vision backbone as S$^2$-MLPv2. Using 55M
parameters, our medium-scale model, S$^2$-MLPv2-Medium achieves an $83.6\%$
top-1 accuracy on the ImageNet-1K benchmark using $224\times 224$ images
without self-attention and external training data.
