Abstract

Deep learning approaches, mostly in the form of convolutional neural networks (CNNs), have taken the field of computer vision by storm. While the progress in recent years has been astounding, their limited interpretability and their demands for ever-larger datasets pose important limitations today. After reviewing some of the challenges in creating large-scale datasets, I will turn to how ideas from classical computer vision can be used to design deep neural networks in computer vision. I will discuss two specific directions. First, I will show how we can adopt techniques from traditional image processing in deep networks for image understanding toward improving their interpretability and accuracy. Second, I will discuss how concepts from optimization and image statistics can be used to design parsimonious network architectures toward improved generalization in a variety of application areas.

Biography

Stefan Roth received the Diplom degree in Computer Science and Engineering from the University of Mannheim, Germany in 2001. In 2003 he received the ScM degree in Computer Science from Brown University, and in 2007 the PhD degree in Computer Science from the same institution.

Since 2007 he is on the faculty of Computer Science at Technische Universität Darmstadt, Germany (Juniorprofessor 2007-2013, Professor since 2013). His research interests include probabilistic and statistical approaches to image modeling, motion estimation and tracking, as well as object recognition and scene understanding. He received several awards, including honorable mentions for the Marr Prize at ICCV 2005 (with M. Black) and ICCV 2013 (with C. Vogel and K.

Schindler), the Olympus-Prize 2010 of the German Association for Pattern Recognition (DAGM), and the Heinz Maier-Leibnitz Prize 2012 of the German Research Foundation (DFG). In 2013, he was awarded a starting grant of the European Research Council (ERC). He regularly serves as an area chair for CVPR, ICCV, and ECCV, and is member of the editorial board of the International Journal of Computer Vision (IJCV), the IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), and PeerJ Computer Science

https://www.visinf.tu-darmstadt.de/team_members/sroth/sroth.en.jsp