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

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

URL: http://arxiv.org/abs/2106.07477v1
Date: Mon, 14 Jun 2021 15:05:11 GMT
Title: S$^2$-MLP: Spatial-Shift MLP Architecture for Vision
Authors: Tan Yu, Xu Li, Yunfeng Cai, Mingming Sun, Ping Li
Abstract summary: Recently, visual Transformer (ViT) and its following works abandon the convolution and exploit the self-attention operation. In this paper, we propose a novel pure architecture, spatial-shift (S$2$-MLP)
Score: 34.47616917228978
License: http://creativecommons.org/licenses/by/4.0/
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 CNN. 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 token-mixing operation in MLP-Mixer is a variant of 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 devise a spatial-shift operation for achieving the communication between patches. It has a local reception field and is spatial-agnostic. Meanwhile, 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.