  We propose RepMLP, a multi-layer-perceptron-style neural network building
block for image recognition, which is composed of a series of fully-connected
(FC) layers. Compared to convolutional layers, FC layers are more efficient,
better at modeling the long-range dependencies and positional patterns, but
worse at capturing the local structures, hence usually less favored for image
recognition. We propose a structural re-parameterization technique that adds
local prior into an FC to make it powerful for image recognition. Specifically,
we construct convolutional layers inside a RepMLP during training and merge
them into the FC for inference. On CIFAR, a simple pure-MLP model shows
performance very close to CNN. By inserting RepMLP in traditional CNN, we
improve ResNets by 1.8% accuracy on ImageNet, 2.9% for face recognition, and
2.3% mIoU on Cityscapes with lower FLOPs. Our intriguing findings highlight
that combining the global representational capacity and positional perception
of FC with the local prior of convolution can improve the performance of neural
network with faster speed on both the tasks with translation invariance (e.g.,
semantic segmentation) and those with aligned images and positional patterns
(e.g., face recognition). The code and models are available at
https://github.com/DingXiaoH/RepMLP.
