In this talk I will present my research on making computers see and think, two abilities essential for deep understanding of pixels. In particular, I will focus on understanding not just visual objects but also their connections. First, I will describe how we built ImageNet, a large-scale visual knowledge base that led to a paradigm shift in computer vision research. Next, I will show the design of a reasoning engine that uses an object relation graph to perform probabilistic logical inference in visual recognition. Third, I will present a general and powerful method for extracting deep semantics from pixels, with state-of-the-art results on multiple challenging tasks. I will conclude my talk by describing ongoing efforts and future directions toward deeper semantics, richer knowledge, and more human-like reasoning.
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. 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 Sloan Research Fellowship, PAMI Mark Everingham Prize, Yahoo ACE Award, Google Faculty Research Award, Amazon Research Award, ICCV Marr Prize, and ECCV Best Paper Award.