Towards deep kernel machines

Jurgen Gall (University of Bonn, Germany)


Analyzing the behavior of humans in continuous video recordings is still a very difficult task. In the fully supervised setting, temporal models like RNNs are trained on videos that are annotated at a frame-level. Acquiring such annotations, however, is very time consuming and strong temporal models require large amounts of annotated training data. Weaker forms of supervision like transcripts are therefore investigated to learn temporal models. In this talk, I will describe some of our recent works on weakly supervised learning of actions and I will give an overview of the research activities that are conducted within the DFG research unit "Anticipating Human Behavior" at the University of Bonn.


Prof. Dr. Juergen Gall is professor and head of the Computer Vision Group at the University of Bonn since June 2013. After his Ph.D. in computer science from the Saarland University and the Max Planck Institut für Informatik, he was a postdoctoral researcher at the Computer Vision Laboratory, ETH Zurich, from 2009 until 2012 and senior research scientist at the Max Planck Institute for Intelligent Systems in Tübingen from 2012 until 2013. He received a grant for an independent Emmy Noether research group from the German Research Foundation (DFG) in 2013, the German Pattern Recognition Award of the German Association for Pattern Recognition (DAGM) in 2014, and an ERC Starting Grant in 2016. He is further spokesperson of the DFG funded research unit "Anticipating Human Behavior" at the University of Bonn since 2017.