ECCV 2022 Tutorial onSelf-Supervised Representation Learning in Computer Vision |
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Tel Aviv
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ECCV 2022 Tutorial onSelf-Supervised Representation Learning in Computer Vision |
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Tel Aviv
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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.
2:05 - 2:15 Welcome and agenda - Xinlei Chen and Christoph Feichtenhofer, Meta AI
2:15 - 3:00 Opening remarks - Yann LeCun, Meta AI and NYU
3:00 - 3:45 Masked autoencoders as scalable vision learners (slides) - Xinlei Chen, Meta AI
3:45 - 4:30 Self-supervised learning from masked video and audio (slides) - Christoph Feichtenhofer, Meta AI
4:30 - 4:45 Coffee Break
4:45 - 5:30 The virtuous cycle of object discovery and representation learning (slides) - Olivier J. Hénaff, DeepMind
5:30 - 6:15 Contrastive learning of visual representations (slides) - Ting Chen, Google
Contact: Xinlei Chen, Christoph Feichtenhofer
