Pyramid Vision Transformer

Pyramid Vision Transformers (PVTs) are a class of deep learning models combining the strengths of convolutional neural networks and vision transformers for image analysis tasks. Current research focuses on improving PVT architectures for specific applications, such as medical image segmentation (e.g., organ segmentation in CT scans, polyp detection), 3D object detection, and egocentric action recognition, often incorporating techniques like low-rank adaptation and dynamic class token generation to enhance efficiency and accuracy. This work is significant because PVTs offer a powerful alternative to purely convolutional or transformer-based approaches, demonstrating strong performance across diverse computer vision problems while addressing limitations like computational cost and high-frequency information capture.