Adrien Gaidon: Self-supervised 3D vision
Channel: Oxford ML and Physics Seminars
Date: 21/05/2021

Is one eye all you need? Can we learn robot perception from raw videos only? Can we get robust 3D depth estimation from a single monocular camera? In this talk, we will discuss recent research progress we made at TRI on self-supervised learning for monocular 3D vision, its uses, limitations, and promising future directions combining self-supervision with other scalable sources of supervision.

Adrien Gaidon is the Head of Machine Learning Research at the Toyota Research Institute (TRI) in Los Altos, CA, USA. Adrien’s research focuses on scaling up ML for robot autonomy, spanning Scene and Behavior Understanding, Simulation for Deep Learning, 3D Computer Vision, and Self-Supervised Learning. He received his PhD from Microsoft Research - Inria Paris in 2012, has over 50 publications and patents in ML/CV/AI, top entries in international Computer Vision competitions, multiple best reviewer awards, international press coverage for his work on Deep Learning with simulation, was a guest editor for the International Journal of Computer Vision, and co-organized multiple workshops on Autonomous Driving at CVPR/ECCV/ICML. You can find him at adriengaidon.com, on linkedin, and Twitter @adnothing.