Vision Transformers (ViTs) have gained significant attention in the field of AI, particularly in image processing tasks, as they leverage transformer architecture originally designed for language processing. Key points to note include: 1) ViTs treat images as sequences of patches, allowing for global context understanding. 2) They often outperform convolutional neural networks (CNNs) when trained on large datasets, signaling a paradigm shift in computer vision. 3) The design and training strategies, such as MLP-based patch pre-processing and self-supervised training approaches, are vital for optimizing ViT performance. These advancements represent a critical evolution in AI, suggesting opportunities for further research and application in various domains.