Towards deep kernel machines
Jurgen Gall
(University of Bonn, Germany)
Abstract:
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.
Bio:
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.