Transformers in Vision

This lecture covers the evolution of visual intelligence models, from traditional architectures to the rise of Transformers in computer vision and natural language processing. It explores the key concepts of Convolutional Neural Networks, Recurrent Neural Networks, and the recent advancements in image classification leaderboards. The lecture delves into the inner workings of Transformers, focusing on multi-headed self-attention mechanisms, positional encodings, and feed-forward layers. It also discusses the importance of attention mechanisms in understanding images and text, showcasing various forms of attention and their applications in visual tasks.