S$^2$-MLP: Spatial-Shift MLP Architecture for Vision

TL;DR

S$^2$-MLP introduces a simple, parameter-free spatial-shift operation in an MLP architecture, achieving high accuracy on ImageNet with fewer parameters and FLOPs, outperforming previous MLP models and rivaling ViT.

Contribution

It proposes a novel spatial-shift MLP architecture that simplifies token communication, improving performance on medium-scale datasets compared to existing MLP models.

Findings

S$^2$-MLP outperforms MLP-Mixer on ImageNet-1K.

Achieves comparable accuracy to ViT with fewer FLOPs and parameters.

Parameter-free spatial-shift operation enhances efficiency and effectiveness.

Abstract

Recently, visual Transformer (ViT) and its following works abandon the convolution and exploit the self-attention operation, attaining a comparable or even higher accuracy than CNNs. More recently, MLP-Mixer abandons both the convolution and the self-attention operation, proposing an architecture containing only MLP layers. To achieve cross-patch communications, it devises an additional token-mixing MLP besides the channel-mixing MLP. It achieves promising results when training on an extremely large-scale dataset. But it cannot achieve as outstanding performance as its CNN and ViT counterparts when training on medium-scale datasets such as ImageNet1K and ImageNet21K. The performance drop of MLP-Mixer motivates us to rethink the token-mixing MLP. We discover that the token-mixing MLP is a variant of the depthwise convolution with a global reception field and spatial-specific configuration. But the global reception field and the spatial-specific property make token-mixing MLP prone to over-fitting. In this paper, we propose a novel pure MLP architecture, spatial-shift MLP (S$^2$-MLP). Different from MLP-Mixer, our S$^2$-MLP only contains channel-mixing MLP. We utilize a spatial-shift operation for communications between patches. It has a local reception field and is spatial-agnostic. It is parameter-free and efficient for computation. The proposed S$^2$-MLP attains higher recognition accuracy than MLP-Mixer when training on ImageNet-1K dataset. Meanwhile, S$^2$-MLP accomplishes as excellent performance as ViT on ImageNet-1K dataset with considerably simpler architecture and fewer FLOPs and parameters.