Explore a 35-minute talk on Vision Transformer and its applications in computer vision. Delve into the breakthrough model architecture, focusing on self-attention and its role in vision. Examine various implementations utilizing Vision Transformer as the main backbone, including applications in recognition, detection, segmentation, multi-modal learning, and scene text recognition. Discover the potential of self-attention beyond transformers in building general-purpose model architectures capable of processing diverse data formats such as text, audio, image, and video. Learn about training techniques, including pre-training on large datasets and knowledge distillation. Investigate the model's performance in semantic segmentation, medical image segmentation, and its parameter, FLOPS, and speed efficiency. Understand the limitations of Vision Transformers and gain insights into recommended open-source implementations.