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Compared to convolutional layers, fully-connected (FC) layers are better atmodeling the long-range dependencies but worse at capturing the local patterns,hence usually less favored for image recognition. In this paper, we propose amethodology, Locality Injection, to incorporate local priors into an FC layervia merging the trained parameters of a parallel conv kernel into the FCkernel. Locality Injection can be viewed as a novel StructuralRe-parameterization method since it equivalently converts the structures viatransforming 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 distinguishesRepMLPNet from the other concurrently proposed vision MLPs. As it producesfeature maps of different levels, it qualifies as a backbone model fordownstream tasks like semantic segmentation. Our results reveal that 1)Locality Injection is a general methodology for MLP models; 2) RepMLPNet hasfavorable accuracy-efficiency trade-off compared to the other MLPs; 3)RepMLPNet is the first MLP that seamlessly transfer to Cityscapes semanticsegmentation. The code and models are available athttps://github.com/DingXiaoH/RepMLP.

RepMLPNet: Hierarchical Vision MLP with Re-parameterized Locality