Machine Learning for Robot Vision
Place: Large Lecture Room
Abstract: Deep learning with vast amounts of data has revolutionized the field of computer vision. Robot vision, however, requires to go beyond the developed methods due to its specific requirements: The amount of data is limited and need to be processed on-the-fly, annotation is incomplete or missing, measurements are sparse and of varying level of certainty, and detection and segmentation tasks need to be performed in real-time. In this talk CVLs approaches to these problems will be presented and explained: online learning, weakly supervised and reinforcement learning, certainty-adaptive data densification, anomaly detection, and modulation-based discriminative segmentation. The results of these methods commonly achieve state-of-the-art results on major benchmarks, such as VOT, DAVIS, Kitti, and CARLA.
Short Bio: Michael Felsberg (MSc 1998, PhD 2002) is Full Professor and the Head of the Computer Vision Laboratory, Linköping University. His research interests include learning and modeling of machine perception. He has published more than 150 reviewed conference papers, journal articles, and book contributions, with in total more than 10,000 citations. He has received awards from the German Pattern Recognition Society in 2000, 2004, and 2005, from the Swedish Society for Automated Image Analysis in 2007 and 2010, from Conference on Information Fusion in 2011 (Honorable Mention), from the CVPR Workshop on Mobile Vision 2014, and from ICPR 2016 (best paper in computer vision). He has achieved top ranks on various challenges (VOT: 3rd 2013, 1st 2014, 2nd 2015; VOT-TIR: 1st 2015; OpenCV Tracking: 1st 2015; KITTI Stereo Odometry: 1st 2015, March). He is regularly Associate Editor for major journals in the field (e.g. JMIV, IMAVIS) and top-tier conferences (e.g. ECCV, BMVC, CVPR). He was Track Chair of the International Conference on Pattern Recognition 2016, General Co-Chair of the DAGM symposium in 2011, General Chair of CAIP 2017, and Program Chair of SCIA 2019.