Location: TPOC-4 room 351
Prof. Stefan Roth (TU Darmstadt)
Supervised learning with deep convolutional networks is the workhorse of the majority of computer vision research today. While much progress has been made already, exploiting deep architectures with standard components, enormous datasets, and massive computational power, I will argue that it pays to scrutinize some of the components of modern deep networks. I will begin with looking at the common pooling operation and show how we can replace standard pooling layers with a perceptually-motivated alternative, with consistent gains in accuracy. Next, I will show how we can leverage self-similarity, a well known concept from the study of natural images, to derive non-local layers for various vision tasks that boost the discriminative power. Finally, I will present a lightweight approach to obtaining predictive probabilities in deep networks, allowing to judge the reliability of the prediction.
Speaker’s bio: 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 deep learning 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