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Learning with discrete MAP-inference models for stereo and motion



Learning with discrete MAP-inference models for stereo and motion


Thomas Pock - Graz University of Technology


MAP inference models (also known as MRF or CRF models) are simple yet powerful discrete optimization models which can be used to solve a number of computer vision tasks. Recently, those models have been outperformed by black-box learning methods based on convolutional neural networks. In this talk, we interpret the MAP inference models as an additional inference layer in the network, hence giving us the ability to impose a well-controlled smoothness prior to the solution. In order to make the MAP inference layer also efficient we propose a highly parallel dual coordinate descent algorithm based on dynamic programming. For learning we make use of a technique similar to the structured output support vector machine which, allows us to perform end2end learning. We show applications for our learned models to stereo and motion estimation. Joint work with A. Shekhovtsov, P. Knöbelreiter, G. Munda, C. Reinbacher


Thomas Pock received his MSc (1998-2004) and his PhD (2005-2008) in Computer Engineering (Telematik) from Graz University of Technology. After a Post-doc position at the University of Bonn, he moved back to Graz University of Technology where he has been an Assistant Professor at the Institute for Computer Graphics and Vision. In 2013 Thomas Pock received the START price of the Austrian Science Fund (FWF) and the German Pattern recognition award of the German association for pattern recognition (DAGM) and in 2014, Thomas Pock received an starting grant from the European Research Council (ERC). Since 2014, Thomas Pock is a Professor of Computer Science at Graz University of Technology and a principal scientist at the Center for Vision, Automation & Control at the Austrian Institute of Technology (AIT). The focus of his research is the development of mathematical models for computer vision and image processing as well as the development of efficient convex and non-smooth optimization algorithms


2019-05-16 at 3:00 pm (subject to variability)