Swin Transformer is a novel deep-learning architecture for computer vision tasks, proposed by Microsoft Research Asia in 2021. It combines the powerful sequence-processing ability of the Transformer model with the efficient local feature extraction advantage of convolutional neural networks. Its key innovation is the "Shifted Window" mechanism, which enables the model to balance performance and computational efficiency, and is suitable for tasks such as image classification, object detection, and semantic segmentation.
