In recent years deep learning systems have become proficient at recognition problems: for example we can build systems that can recognize objects in images with high accuracy. But recognition is only the first step toward intelligent behavior. I am interested in solving problems that move from recognition to reasoning: systems should be able not only to recognize, but also to perform higher-level tasks based on what they perceive. I will showcase two projects that aim to jointly recognize and reason using end-to-end deep learning systems: Mesh R-CNN, in which we jointly detect objects and predict 3D triangle meshes; and PHYRE, a new benchmark for physical reasoning.

Justin Johnson is an Assistant Professor of Computer Science and Engineering at the University of Michigan, Ann Arbor. Prior to that he was a Research Scientist at Facebook AI Research. He completed his PhD at Stanford University, advised by Fei-Fei Li. His research interests lie primarily in computer vision and include visual reasoning, image synthesis, and 3D perception.