Head of Computer Vision & Learning Group,
Ludwig Maximilian University of Munich
Tel.(office): +49 (0)89/2180-73431
Tel.(secret.): +49 (0)89/2180-73430
FAX: +49 (0)89/2180-9973431
Email: b.ommer (at) lmu (dot) de
»» Open PhD and PostDoc Positions
»» our recent work on Stable Diffusion
Björn Ommer is a full professor at LMU where he heads the Computer Vision & Learning Group (previously Computer Vision Group Heidelberg). Before he was a full professor at the Department of Mathematics and Computer Science of Heidelberg University and also served as a one of the directors of the Interdisciplinary Center for Scientific Computing (IWR) and of the Heidelberg Collaboratory for Image Processing (HCI).
He has studied computer science together with physics as a minor subject at the University of Bonn, Germany. After that he pursued his doctoral studies in computer science at ETH Zurich. He received his Ph.D. degree from ETH Zurich for his dissertation “Learning the Compositional Nature of Objects for Visual Recognition” which was awarded the ETH Medal. Thereafter, Björn held a post-doc position in the Computer Vision Group of Jitendra Malik at UC Berkeley.
He serves as an associate editor for the journal IEEE T-PAMI and previously for Pattern Recognition Letters. Björn is an ELLIS member and ELLIS unit faculty of the ELLIS unit Munich and a PI of the Munich Center for Machine Learning (MCML). He has served as Area Chair for multiple CVPR, ICCV, and ECCV conferences and as workshop and tutorial organizer at these venues.
All aspects of semantic image and video understanding based on (deep) machine learning;
esp.: generative approaches for visual synthesis (e.g. Stable Diffusion), invertible deep models for explainable AI, deep metric and representation learning, and self-supervised learning paradigms and their interdisciplinary applications in the digital humanities and neurosciences.
Please see the publications page.
PhD or Master Thesis Supervision
I have the privilege of working with a great team who are incredibly gifted and nice to be with and our lab is located in beautiful downtown Munich. If you want to join: I am supervising Master and PhD students and PostDocs on topics of Computer Vision and Machine Learning in the faculty of Mathematics, Informatics, and Statistics at LMU.
If you are interested please see our open positions.
Selected Talks and Publications in Popular Science
Stable Diffusion in the Press, (CVPR’22 oral).
Visual Synthesis for Understanding our (Visual) World, Talk @ TUM AI Lecture Series, 2022
These Neural Networks Have Superpowers!, Video on Two-Minute-Papers about our VQGAN paper (CVPR’21 oral).
Self-Supervision: Learning to Learn, Talk @ Heidelberg AI, 01/2019.
Das Objekt jenseits der Digitalisierung, The Future of the Digital Humanities beyond Digitization, Talk @ Deutsches Museum, 12/2018.
AI Learned How To Generate Human Appearance, Video on Two-Minute-Papers about our CVPR’18 paper on disentangling human behavior and appearance.
Painter AI Fools Art Historians, Video on Two-Minute-Papers about our ECCV’18 paper on artistic style transfer.
Improving Stroke Treatment Through Machine Learning, report on interdisciplinary project with neuroscientists from ETH Zurich.
Björn Ommer, From Chaos to Image – The Grammar of Patterns, in: Universitas 68(810): 46-55, 2013.
Björn Ommer, Vom Pixel zum Bild – Wie Computer das Sehen lernen und die Forschungsarbeiten von Geistes- und Naturwissenschaftlern unterstützen können, in: Ruperto Carola Magazine, 02/2011.
Automatische Bildanalyse – Blinde Computer sollen sehen lernen, in: Spiegel Online news report, 22.07.2011.