Fig. 5:

(a): Swin Transformer creates hierarchical feature maps by merging image patches. The self-attention is computed within each local 3D window (the red box). The feature maps generated at each resolution are sent into a ConvNet decoder to produce an output. (b): The 3D cyclic shift of local windows for shifted-window-based self-attention computation.