Greg Shakhnarovich

TTI-Chicago

 

Abstract
I will describe recent work on building and using rich representations aimed at automatic analysis of visual scenes. In particular, I will describe methods for semantic segmentation (labeling regions of an image according to the category it belongs to), and on semantic boundary detection (recovering accurate boundaries of semantically meaningful regions, such as those corresponding to objects). We focus on feed-forward architectures for these tasks, leveraging recent advances in the art of training deep neural networks. Our approach aims to shift the burden of inducing desirable constraints from explicit structure in the model to implicit structure inherent in computing richer, context-aware representations. I will describe experiments on standard benchmark data sets that demonstrate the success of this approach. Joint work with Mohammadreza Mostajabi, Payman Yadollahpour, and Harry Yang.

Bio:
Greg is an Assistant Professor at TTI-Chicago, a philanthropically endowed academic computer science institute located on the University of Chicago campus, where he works on computer vision and machine learning. He also holds a part-time faculty appointment at the University of Chicago Department of Computer Science. Prior to coming to TTI-Chicago, Greg was a post-doctoral researcher at Brown University. He received his PhD degree in Electrical Engineering and Computer Science from MIT in 2005.