RepMLPNet: Hierarchical Vision MLP with Re-parameterized Locality

TL;DR

This paper introduces RepMLPNet, a hierarchical vision MLP architecture that incorporates local priors via Locality Injection, achieving a good accuracy-efficiency balance and effective transfer to semantic segmentation tasks.

Contribution

It proposes Locality Injection as a novel structural re-parameterization method and introduces RepMLPNet, a hierarchical MLP backbone for vision tasks.

Findings

Locality Injection effectively incorporates local priors into MLPs.

RepMLPNet outperforms other MLPs in accuracy-efficiency trade-offs.

RepMLPNet successfully transfers to Cityscapes semantic segmentation.

Abstract

Compared to convolutional layers, fully-connected (FC) layers are better at modeling the long-range dependencies but worse at capturing the local patterns, hence usually less favored for image recognition. In this paper, we propose a methodology, Locality Injection, to incorporate local priors into an FC layer via merging the trained parameters of a parallel conv kernel into the FC kernel. Locality Injection can be viewed as a novel Structural Re-parameterization method since it equivalently converts the structures via transforming the parameters. Based on that, we propose a multi-layer-perceptron (MLP) block named RepMLP Block, which uses three FC layers to extract features, and a novel architecture named RepMLPNet. The hierarchical design distinguishes RepMLPNet from the other concurrently proposed vision MLPs. As it produces feature maps of different levels, it qualifies as a backbone model for downstream tasks like semantic segmentation. Our results reveal that 1) Locality Injection is a general methodology for MLP models; 2) RepMLPNet has favorable accuracy-efficiency trade-off compared to the other MLPs; 3) RepMLPNet is the first MLP that seamlessly transfer to Cityscapes semantic segmentation. The code and models are available at https://github.com/DingXiaoH/RepMLP.