Swin Transformer

The Swin Transformer is a hierarchical vision transformer architecture designed to efficiently process images by dividing them into smaller windows for parallel processing, while still capturing long-range dependencies. Current research focuses on applying Swin Transformers to diverse tasks, including medical image analysis (e.g., disease classification, segmentation), remote sensing (e.g., terrain recognition, anomaly detection), and image manipulation (e.g., super-resolution, watermarking), often incorporating enhancements like attention modules and multi-modal fusion. This versatility makes the Swin Transformer a significant tool for computer vision, improving accuracy and efficiency across a wide range of applications.