Date: June 16, 2016 10:30-12:00
Speaker: Christoph Lampert
Institute of Science and Technology Austria (IST Austria)
Location: TU Vienna
Zemanek Lecture Room (Room Number: HHEG01)
1040 Vienna, Favoritenstraße 9-11, Stiege III, ground floor, light green area
It is a long-lasting dream of computer vision research to build an automatic system that is able to understand natural images on a similar semantic level as a human would. It is clear, however, that our current techniques will not sufficient to achieve this goal, since they need a lot of manually annotated training data for every task they try to solve. In my talk, I will highlight two recent results from our group that rely on transfer learning to overcome this limitation: multi-task learning with active task selection, and weakly-supervised semantic image segmentation.
Christoph Lampert is a professor for Computer Vision and Machine Learning at the Institute of Science and Technology Austria (IST Austria). He received a PhD degree in mathematics from the University of Bonn in 2003. Subsequently, he held postdoctoral positions at the German Research Center for Artificial Intelligence and the Max-Planck for Biological Cybernetics before joining IST in 2010.
His research on computer vision and machine learning has won several international and national awards, including best paper prizes at CVPR and ECCV in 2008. In 2012 he was awarded an ERC Starting Grant by the European Research Council. He is an Editor of the International Journal of Computer Vision (IJCV), Action Editor of the Journal for Machine Learning Research (JMLR), and Associate Editor in Chief of the IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI).
Download the presentation slides.