  An Axial Shifted MLP architecture (AS-MLP) is proposed in this paper.
Different from MLP-Mixer, where the global spatial feature is encoded for
information flow through matrix transposition and one token-mixing MLP, we pay
more attention to the local features interaction. By axially shifting channels
of the feature map, AS-MLP is able to obtain the information flow from
different axial directions, which captures the local dependencies. Such an
operation enables us to utilize a pure MLP architecture to achieve the same
local receptive field as CNN-like architecture. We can also design the
receptive field size and dilation of blocks of AS-MLP, etc, in the same spirit
of convolutional neural networks. With the proposed AS-MLP architecture, our
model obtains 83.3% Top-1 accuracy with 88M parameters and 15.2 GFLOPs on the
ImageNet-1K dataset. Such a simple yet effective architecture outperforms all
MLP-based architectures and achieves competitive performance compared to the
transformer-based architectures (e.g., Swin Transformer) even with slightly
lower FLOPs. In addition, AS-MLP is also the first MLP-based architecture to be
applied to the downstream tasks (e.g., object detection and semantic
segmentation). The experimental results are also impressive. Our proposed
AS-MLP obtains 51.5 mAP on the COCO validation set and 49.5 MS mIoU on the
ADE20K dataset, which is competitive compared to the transformer-based
architectures. Our AS-MLP establishes a strong baseline of MLP-based
architecture. Code is available at https://github.com/svip-lab/AS-MLP.
