This paper proposes a novel vision Transformer architecture called Swin Transformer which utilizes a shifted window approach for computing self-attention, resulting in linear computational complexity. Experiments on ImageNet, COCO, and ADE20K datasets demonstrate state-of-the-art performance across image classification, object detection, and semantic segmentation tasks.

The authors are affiliated with Microsoft Research Asia. While no direct conflict is apparent from the paper itself, potential conflicts related to Microsoft's business interests in computer vision cannot be completely ruled out.

This paper presents a novel and impactful architecture for vision transformers, showing substantial improvements in various vision tasks. The shifted window approach offers a compelling solution to the computational challenges of traditional transformers, making it suitable for a wider range of applications. However, the limited scope of evaluation tasks and lacking discussion on the limitations reduce the rating from a 5.