  Vision Transformer (ViT) attains state-of-the-art performance in visual
recognition, and the variant, Local Vision Transformer, makes further
improvements. The major component in Local Vision Transformer, local attention,
performs the attention separately over small local windows. We rephrase local
attention as a channel-wise locally-connected layer and analyze it from two
network regularization manners, sparse connectivity and weight sharing, as well
as weight computation. Sparse connectivity: there is no connection across
channels, and each position is connected to the positions within a small local
window. Weight sharing: the connection weights for one position are shared
across channels or within each group of channels. Dynamic weight: the
connection weights are dynamically predicted according to each image instance.
We point out that local attention resembles depth-wise convolution and its
dynamic version in sparse connectivity. The main difference lies in weight
sharing - depth-wise convolution shares connection weights (kernel weights)
across spatial positions. We empirically observe that the models based on
depth-wise convolution and the dynamic variant with lower computation
complexity perform on-par with or sometimes slightly better than Swin
Transformer, an instance of Local Vision Transformer, for ImageNet
classification, COCO object detection and ADE semantic segmentation. These
observations suggest that Local Vision Transformer takes advantage of two
regularization forms and dynamic weight to increase the network capacity. Code
is available at https://github.com/Atten4Vis/DemystifyLocalViT.
