log in  |  register  |  feedback?  |  help  |  web accessibility
Learning to Understand Visual Data with Minimal Human Supervision
4105 Iribe
Thursday, April 4, 2019, 11:00 am-12:00 pm Calendar
  • You are subscribed to this talk through .
  • You are watching this talk through .
  • You are subscribed to this talk. (unsubscribe, watch)
  • You are watching this talk. (unwatch, subscribe)
  • You are not subscribed to this talk. (watch, subscribe)

Humans and animals learn to see the world mostly on their own, without supervision, yet today’s state-of-the-art visual recognition systems rely on millions of manually-annotated training images. This reliance on labeled data has become one of the key bottlenecks in creating systems that can attain a human-level understanding of the vast concepts and complexities of our visual world. Indeed, while computer vision research has made tremendous progress, most success stories are limited to specific domains in which lots of carefully-labeled data can be unambiguously and easily acquired.

In this talk, I will present my research in computer vision and deep learning on creating scalable recognition systems that can learn to understand visual data with minimal human supervision. Given the right constraints, I’ll show that one can design learning algorithms that discover and generate meaningful patterns from the data with little to no human supervision. In particular, I’ll focus on algorithms that can localize relevant image regions given only weak image/video-level supervision; hierarchically disentangle and generate fine-grained details of objects; and anonymize sensitive video regions for privacy-preserving visual recognition. I’ll conclude by discussing remaining challenges and future directions.


Yong Jae Lee is an Assistant Professor in the Department of Computer Science at the University of California, Davis. His research interests are in computer vision, machine learning, and computer graphics, with a focus on creating robust visual recognition systems that can learn to understand the visual world with minimal human supervision. Before joining UC Davis in 2014, he received his Ph.D. from the University of Texas at Austin in 2012 advised by Kristen Grauman, and was a post-doc at Carnegie Mellon University (2012-2013) and UC Berkeley (2013-2014) advised by Alyosha Efros. He received his B.S. in Electrical Engineering from the University of Illinois at Urbana-Champaign in 2006. He is a recipient of several awards including the Army Research Office (ARO) Young Investigator Program (YIP) award, UC Davis Hellman Foundation Fellowship, National Science Foundation (NSF) CAREER award, and AWS Machine Learning Research Award.

This talk is organized by Brandi Adams