Vision Transformer Architecture
Vision Transformers (ViTs) adapt the Transformer architecture, initially successful in natural language processing, to image analysis, aiming to improve upon convolutional neural networks (CNNs) by capturing long-range dependencies within images. Current research focuses on enhancing ViT efficiency through techniques like adaptive token sampling, sparse regularization, and hybrid CNN-Transformer designs, as well as exploring their application in diverse fields such as medical image registration, weather forecasting, and object recognition in challenging scenarios (e.g., UAV imagery). The resulting models demonstrate strong performance across various computer vision tasks, offering a powerful alternative to traditional CNNs and impacting fields requiring high-accuracy image analysis.