Self-supervised vision

An overview of the use of self-supervised learning in Computer Vision.

Timestamps:
00:00 - Self-supervised vision
00:29 - Self-supervised learning - motivation
01:39 - Self-supervised learning - motivation (cont.)
04:11 - Self-supervised learning - creating your own supervision
05:38 - Self-supervised learning - creating your own supervision (cont.)
06:32 - Self-supervised learning - creating your own supervision (cont. cont.)
07:36 - Self-supervised learning - context as supervision
09:09 - Back to vision: context as supervision
11:39 - Pretext task: inpainting
13:30 - Pretext task: jigsaw puzzles
14:32 - Pretext task: colourisation
16:00 - What's wrong with L2?
17:29 - Pretext task: counting
19:53 - Grouping/Common fate
20:36 - Pretext task: Grouping/Common fate
22:06 - Pretext task: Rotations
23:28 - Pretext task: Clustering
25:00 - Contrastive Learning
26:47 - Masked Autoencoders

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:    • Self-supervised learning and pseudo-labelling  


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/
YouTube:    / @samuelalbanie1