ICCV 2023 Tutorial onSelf-Supervised Learning of Visual Representations |
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Paris
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ICCV 2023 Tutorial onSelf-Supervised Learning of Visual Representations |
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Paris
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This tutorial covers popular approaches and recent advancements in the field of self-supervised visual representation learning. We will cover topics such as Masked Autoencoders and Contrastive Learning. We will show how such frameworks are successfully learning from 2D static image and dynamic video information. Finally, we will also discuss self-supervised learning from a machine learning perspective. Overall, we will show connections and distinctions between different techniques for self-supervised learning, and provide insights about popular approaches in the community.
1:35 - 1:45 Welcome and agenda - Xinlei Chen and Christoph Feichtenhofer, Meta
1:45 - 2:30 Opening remarks - Alyosha Efros, UC Berkeley
2:30 - 3:15 Reconstructive and non-reconstructive self-supervised learning for images - Xinlei Chen, Meta
3:15 - 3:30 Coffee Break
3:30 - 4:15 Self-supervised learning from video and audio - Christoph Feichtenhofer, Meta
4:15 - 5:00 Self-supervised scene understanding, human alignment, and memory retrieval - Olivier J. Hénaff, Google DeepMind
5:00 - 5:45 Self-supervised pre-training for robotics - Ilija Radosavovic, UC Berkeley
Contact: Xinlei Chen, Christoph Feichtenhofer
