Meeting ID: 862 3823 3298
Title: Robust Multi-Task Vision
Abstract: I will open the talk with a discussion on understanding and developing perception in conjunction with downstream action. I will follow by showing our latest works on this front, toward a robust multi-task vision. Namely, mutliMAE, Omnidata, and 3D common corruptions. If we have time, I will show an overview of a few other projects and will end with an open-ended discussion.
Bio: Amir Zamir is an Assistant Professor of Computer Science at the Swiss Federal Institute of Technology (EPFL). His research interests are in computer vision, machine learning, and perception-for-robotics. Prior to joining EPFL in 2020, he spent time at UC Berkeley, Stanford, and UCF. He has been recognized with CVPR 2018 Best Paper Award, CVPR 2016 Best Student Paper Award, CVPR 2020 Best Paper Award Nomination, and NVIDIA Pioneering Research Award 2018. His research has been covered by popular press outlets, such as The New York Times or Forbes.
Publications, project pages, code: https://vilab.epfl.ch/zamir/