Abstract

Achieving human-level visual understanding requires extracting deeper semantics from images. In particular, it entails moving beyond detecting objects to understanding the relations between them. It also demands progress in mid-level vision, which extracts deeper geometric information such as pose and 3D. In this talk I will present recent work on both fronts. I will describe efforts on recognizing human-object interactions, an important type of relations between visual entities. I will present a state-of-the-art method on human pose estimation. Finally, I will discuss recovering 3D from a single image, a fundamental mid-level vision problem.

Bio

Jia Deng is an Assistant Professor of Computer Science and Engineering at the University of Michigan. His research focus is on computer vision and machine learning, in particular, achieving human-level visual understanding by integrating perception, cognition, and learning. He received his Ph.D. from Princeton University and his B.Eng. from Tsinghua University, both in computer science. He is a recipient of the Yahoo ACE Award, a Google Faculty Research Award, the ICCV Marr Prize, and the ECCV Best Paper Award.

All faculties and students are welcome to join.