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 (EncoderOnly)
06:50 Transformer Encoder
08:04 SingleHead Attention
11:45 MultiHead Attention
13:36 MultiLayer 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:    • Neural network architectures, scaling...  

Links:
Slides (pdf): https://samuelalbanie.com/files/diges...

References for papers mentioned in the video can be found at
http://samuelalbanie.com/digests/2023...


For related content:
Twitter:   / samuelalbanie  
personal webpage: https://samuelalbanie.com/
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