Last decade's deep learning revolution in part began in the area of computer vision. The intrinsic complexity of visual perception problems urged the community to explore effective methods for learning abstractions from data. In this talk, I will review a few major breakthroughs that stemmed from computer vision. I will discuss my work on Deep Residual Networks (ResNets) that enabled deep learning to get way deeper, and its influence on the broader artificial intelligence areas over the years. I will also review my work on enabling deep learning to solve complex object detection and segmentation problems in simple and intuitive ways.
On top of this progress, I will introduce recent research on learning from visual observations without human supervision, a topic known as visual self-supervised learning. I will discuss my research that contributed to shaping the two frontier directions on this topic. This research sheds light on future directions. I will discuss the opportunities for self-supervised learning in the visual world. I will also discuss how the research on computer vision may continue influencing broader areas, e.g., by generalizing self-supervised learning to scientific observations from nature.
Bio: Kaiming He is a Research Scientist Director at Facebook AI Research (FAIR). Before joining FAIR in 2016, he was with Microsoft Research Asia from 2011 to 2016. He received his PhD degree from the Chinese University of Hong Kong in 2011, and his B.S. degree from Tsinghua University in 2007. His research areas include deep learning and computer vision. He is best-known for his work on Deep Residual Networks (ResNets), which have made significant impact on computer vision and broader artificial intelligence. He received several outstanding paper awards at top-tier conferences, including CVPR, ICCV, and ECCV. He received the PAMI Young Researcher Award in 2018. His publications have over 400,000 citations.
To request accommodations for a disability please contact Emily Lawrence, emilyl@cs.princeton.edu, at least one week prior to the event.