Various Vision Transformer

Vision Transformers (ViTs) adapt the transformer architecture, known for natural language processing, to computer vision tasks. Current research focuses on improving ViT efficiency through techniques like learnable token merging, pruning less important model components, and optimizing attention mechanisms for faster inference and reduced computational cost. These advancements aim to make ViTs more practical for resource-constrained applications while maintaining or improving accuracy, impacting various computer vision domains including image classification, object detection, and semantic segmentation. The development of more efficient and effective ViTs is a significant area of ongoing research, driving improvements in both the theoretical understanding and practical deployment of these powerful models.