Visual Scene Understanding with Deep Learning
Published：2019-07-10
Speaker: Michael Ying Yang
Time and Date: 1:30 pm, July 10, 2019
Place: Room 1101 of East Main Building of Guanghua Building, Handan Campus, Fudan University
Abstract:
Inspired by the ability of humans to interpret and understand visual scenes nearly effortlessly, the problem of visual scene understanding has long been advocated as the holy grail of computer vision. In recent years there has been considerable progress on many sub-problems of the overall scene understanding problem. Due to the rise of deep learning, the performance for these sub-tasks starts to achieve remarkable performance levels. This talk highlights recent progress on some essential components such as object recognition and change detection.
Biography:
Michael Ying Yang is currently Assistant Professor with University of Twente (the Netherlands), heading a group working on scene understanding. He received the PhD degree from University of Bonn (Germany) in 2011. From 2012 to 2015, he was a Postdoc at Leibniz University Hannover. From 2015 to 2016, he was a Senior Researcher at TU Dresden. His research interests are in the fields of computer vision and photogrammetry with specialization on scene understanding and semantic interpretation from imagery. He published over 80 articles in international journals and conference proceedings and co-supervise 7 PhD students. He serves as Program Chair of ISPRS Geospatial Week 2019, Associate Editor of Photogrammetric Engineering & Remote Sensing, Editorial Advisory Board member of ISPRS Journal of Photogrammetry and Remote Sensing, co-chair of ISPRS working group II/5 Dynamic Scene Analysis, and recipient of the ISPRS President's Honorary Citation (2016) and Best Science Paper Award at BMVC 2016. Since 2016, he is a Senior Member of IEEE.