Events

In Pursuit of Visual Intelligence

Speaker: Kaiming He

Location: 60 Fifth Avenue, Room C15

Date: Friday, April 28, 2023

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.