I will first give a brief introduction of the computer vision group at UC Davis, including the research directions of each member. I will then present our group's recent and ongoing work on weakly-supervised visual recognition. In contrast to fully-supervised algorithms, the proposed methods do not require detailed annotations during training, and instead can learn to focus on the relevant visual regions given only image-level or video-level semantic tags that state whether an object is present or absent (e.g. an image tagged with "car"). I will show how the proposed algorithms can produce state-of-the-art weakly-supervised results for object localization and detection.

Yong Jae Lee is an Assistant Professor in the Department of Computer Science at the University of California, Davis. His research in computer vision and machine learning focuses on object recognition and video understanding. Before joining UC Davis in 2014, he spent one year as a post-doc at UC Berkeley and one year as a post-doc at Carnegie Mellon University, both under the supervision of Alyosha Efros. He received his PhD from the University of Texas at Austin in 2012 under the supervision of Kristen Grauman and his BS from University of Illinois at Urbana-Champaign in 2006.