Event Location: Newell Simon Hall 1507
Bio: David Fouhey is a Ph.D. student at the Robotics Institute of Carnegie Mellon University, where he is advised by Abhinav Gupta and Martial Hebert. His research interests include computer vision and machine learning with a particular focus on scene understanding. David’s work has been supported by both NSF and NDSEG fellowships. He has spent time at Microsoft Research and University of Oxford’s Visual Geometry Group.

Abstract: One primary goal of AI from its very beginning has been to develop systems that can understand an image in a meaningful way. While we have seen tremendous progress in recent years on naming-style tasks like image classification or object detection, a meaningful understanding requires going beyond this paradigm. Scenes are inherently 3D, so our understanding must also capture the underlying 3D and physical properties. Additionally, our understanding must be human-centric since any man-made scene has been built with humans in mind. Despite the importance of obtaining a 3D and human-centric understanding, we are only beginning to scratch the surface on both fronts: many fundamental questions, in terms of how to both frame and solve the problem, remain unanswered.

In this talk, I will discuss my efforts towards building a physical and human-centric understanding of images. I will present work addressing the questions: (1) what 3D properties should we model and predict from images, and do we actually need explicit 3D training data to do this? (2) how can we reconcile data-driven learning techniques with the physical constraints that exist in the world? and (3) how can understanding humans improve traditional 3D and object recognition tasks?