Swin Transformer: Hierarchical Vision Transformer using Shifted Windows

Swin Transformer is a type of hierarchical vision transformer that uses shifted windows to improve the efficiency of processing images. The traditional vision transformer processes images by dividing them into smaller patches, which are then fed into a transformer network. However, this approach can be computationally expensive, as the number of patches can be quite large for high-resolution images.

Swin Transformer addresses this issue by using a hierarchical approach, where the image is first divided into larger patches. These patches are then processed by a smaller transformer network, which produces feature maps that are used to further divide the image into smaller patches. This process is repeated multiple times, with each stage processing smaller and smaller patches to produce increasingly detailed feature maps.

In addition to this hierarchical approach, Swin Transformer also uses shifted windows to further reduce the number of patches that need to be processed. Rather than dividing the image into regular patches, the windows are shifted by a certain amount, leading to overlapping patches. This approach reduces the number of patches needed to represent the image, while still maintaining the ability to capture spatial information.

Overall, Swin Transformer has shown promising results on image classification tasks, achieving state-of-the-art performance on several benchmarks while requiring less computational resources than previous approaches.