X-MLP: A Patch Embedding-Free MLP Architecture for Vision
Xinyue Wang Zhicheng Cai Chenglei Peng
Convolutional neural networks (CNNs) and vision transformers (ViT) have
obtained great achievements in computer vision. Recently, the research of
multi-layer perceptron (MLP) architectures for vision have been popular again.
Vision MLPs are designed to be independent from convolutions and self-attention
operations. However, existing vision MLP architectures always depend on
convolution for patch embedding. Thus we propose X-MLP, an architecture
constructed absolutely upon fully connected layers and free from patch
embedding. It decouples the features extremely and utilizes MLPs to interact
the information across the dimension of width, height and channel independently
and alternately. X-MLP is tested on ten benchmark datasets, all obtaining
better performance than other vision MLP models. It even surpasses CNNs by a
clear margin on various dataset. Furthermore, through mathematically restoring
the spatial weights, we visualize the information communication between any
couples of pixels in the feature map and observe the phenomenon of capturing
long-range dependency.