Vision Transformer Basics

An introduction to the use of transformers in Computer vision. Timestamps: 00:00 - Vision Transformer Basics 01:06 - Why Care about Neural Network Architectures? 02:40 - Attention is all you need 03:56 - What is a Transformer? 05:16 - ViT: Vision Transformer (Encoder-Only) 06:50 - Transformer Encoder 08:04 - Single-Head Attention 11:45 - Multi-Head Attention 13:36 - Multi-Layer Perceptron 14:45 - Residual Connections 16:31 - LayerNorm 18:14 - Position Embeddings 20:25 - Cross/Causal Attention 22:14 - Scaling Up 23:03 - Scaling Up Further 23:34 - What factors are enabling effective further scaling? 24:29 - The importance of scale 26:04 - Transformer scaling laws for natural language 27:00 - Transformer scaling laws for natural language (cont.) 27:54 - Scaling Vision Transformer 29:44 - Vision Transformer and Learned Locality Topics: #computervision #ai #introduction Notes: This lecture was given as part of the 2022/2023 4F12 course at the University of Cambridge. It is an update to a previous lecture, which can be found here: https://www.youtube.com/watch?app=desktop&v=FyM0rYUvFbM Links: Slides (pdf): https://samuelalbanie.com/files/digest-slides/2023-11-vision-transformer-basics.pdf References for papers mentioned in the video can be found at http://samuelalbanie.com/digests/2023-11-vision-transformer-basics For related content: - Twitter: https://twitter.com/SamuelAlbanie - personal webpage: https://samuelalbanie.com/ - YouTube: https://www.youtube.com/@SamuelAlbanie1