Vision Transformer Model

Vision Transformers (ViTs) adapt the Transformer architecture, initially successful in natural language processing, to image analysis. Current research focuses on improving ViT efficiency through techniques like model pruning, quantization, and the development of specialized architectures such as Swin Transformers and Steerable Transformers, addressing computational limitations for real-time applications and resource-constrained devices. ViTs are demonstrating state-of-the-art performance across diverse applications, including medical image analysis, malware detection, and action recognition, highlighting their potential to revolutionize computer vision tasks. The ongoing emphasis is on balancing accuracy with computational efficiency to enable broader deployment.