Self-Supervision: Learning to Learn

Prof. Bjoern Ommer
Head of Computer Vision Group, Heidelberg University

January 22, 2019


A major challenge of artificial intelligence is to learn models that generalize to novel data. While training images and videos are easily available, labels are not, thus motivating self-supervised learning. Furthermore, Prof. Ommer will present a widely applicable strategy based on deep reinforcement learning to improve self-supervision. As a challenging application we will discuss estimating human pose. Time permitting, Prof. Ommer will present a variety of applications of this research ranging from behavior analysis in neuroscience to data analysis in the digital humanities.


Björn Ommer is a full professor for Scientific Computing and leads the Computer Vision Group at Heidelberg University. He has studied computer science together with physics as a minor subject at the University of Bonn, Germany. His diploma (~M.Sc.) thesis focused on visual grouping based on perceptual organization and compositionality. After that he pursued his doctoral studies at ETH Zurich Switzerland in the Pattern Analysis and Machine Learning Group headed by Joachim M. Buhmann. He received his Ph.D. degree from ETH Zurich in 2007 for his dissertation "Learning the Compositional Nature of Objects for Visual Recognition" which was awarded the ETH Medal. Thereafter, Björn held a post-doc position in the Computer Vision Group of Jitendra Malik at UC Berkeley. He serves as an associate editor for the journal IEEE T-PAMI and previously for Pattern Recognition Letters. Björn is one of the directors of the HCI and of the IWR, principle investigator in the research training group 1653 ("Spatio/Temporal Graphical Models and Applications in Image Analysis"), and a member of the executive board and scientific committee of the Heidelberg Graduate School HGS MathComp. He has received the Outstanding Reviewer Award at ICCV'15, CVPR'14, ICCV'13, CVPR'11, and CVPR'10 and has served as Area Chair for ECCV'18. Björn has organized the 2011 DAGM Workshop on Unsolved Problems in Pattern Recognition.

Event Info

The event will take place on Tuesday, 22 January, 2019 at 7pm at the Gästehaus Uni Heidelberg (guest house Uni Heidelberg), Im Neuenheimer Feld 370. Drinks and snacks will be provided, courtesy of the Division of Medical Image Computing at DKFZ. Kindly help us plan ahead by registering for the event on our meetup page.