Heidelberg Collaboratory for Image Processing (HCI) &
Interdisciplinary Center for Scientific Computing (IWR),
Heidelberg University
Mathematikon (INF 205), Room 4.321
HCI / IWR, Uni Heidelberg
D-69120 Heidelberg, Germany
Tel.(office): +49 6221/54-14806
Tel.(secret.): +49 6221/54-14807
Fax: +49 6221/54-14814
Email: ommer (at) uni-heidelberg (dot) de
Open PhD and PostDoc Positions in Computer Vision

Brief C.V.
Björn Ommer is a full professor for Scientific Computing and leads the Computer Vision Group at Heidelberg University.
He has studied computer science together with physics as a minor subject at the University of Bonn, Germany. His diploma (~M.Sc.) thesis focused on visual grouping based on perceptual organization and compositionality.
After that he pursued his doctoral studies at ETH Zurich Switzerland in the Pattern Analysis and Machine Learning Group headed by Joachim M. Buhmann. He received his Ph.D. degree from ETH Zurich in 2007 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 one of the directors of the HCI and of the IWR, part of the ELLIS unit Heidelberg, principle investigator in the research training group 1653 ("Spatio/Temporal Graphical Models and Applications in Image Analysis"), and a member of the executive board and scientific committee of the Heidelberg Graduate School HGS MathComp. He has served as Area Chair for ICCV'21, CVPR'20, and ECCV'18 and organized the 2011 DAGM Workshop on Unsolved Problems in Pattern Recognition.
Research Interests
Computer vision, machine learning, cognitive science, biomedical image analysis, and the digital humanities; esp.:
semantic scene understanding, visual synthesis and interpretable AI, deep learning & self-supervision, deep metric and representation learning, object recognition in images and videos, behavior analysis, and their interdisciplinary applications.
Publications
Main publications' list »» Publications of the Ommer lab
News:- NeurIPS'20 ORAL on cINNs for Network-to-Network Translation
- T-PAMI publication accepted on
- Shared feature learning for Deep Metric Learning
- PLoS ONE publication on weakly supervised transliteration alignment for cuneiform sign detection
- GCPR'20 ORAL on unsupervised part learning by disentangling
- 2 papers accepted at ECCV'20 on:
- Explainable AI and semantic image manipulation
- Deep Metric Learning beyond discriminative features
- ICML'20 paper accepted on
- Generalization in Deep Metric Learning
- Best Paper Award at CVPR'20—AI for Content Creation WS on
- Interpretable Models for Visual Synthesis
- 3 papers accepted at CVPR'20 on:
- Explainable AI
- Reinforcement Learning for Deep Metric Learning
- Unsupervised Behavior Analytics.
- 3 papers accepted at ICCV'19
- Best paper finalist at CVPR'19
- 3 papers accepted at CVPR'19
Selected Reports and Publications in Popular Science
"Das Objekt jenseits der Digitalisierung“, Deutsches Museum, 12/2018, The Future of the Digital Humanities beyond Digitization.
"Der Geist aus dem Computer“, Bild der Wissenschaft, 10/2018, covering part of our work in the digital humanities.
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.
Improving Motor Skills after Stroke, report on interdisciplinary project with neuroscientists from ETH Zurich.
TV documentary on our interdisciplinary work featured by RNF Television.
Björn Ommer, Bilder im Chaos, in: Universitas 68(810): 46-55, 2013.
Björn Ommer, From Chaos to Image - The Grammar of Patterns, in: Ruperto Carola Magazine, 03/2013.
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.
Image Recognition: Teaching Computers to See, in: Young Talents -Innovative Ideas - Viable Alliances, 2011.
Automatische Bildanalyse - Blinde Computer sollen sehen lernen, in: Spiegel Online news report, 22.07.2011.
Dem Computer das Sehen beibringen, in: Rhein-Neckar-Zeitung newspaper article, 19.04.2010.
Teaching
Computer Vision Group: Teaching Website
Links
Computer Vision Group @ Uni Heidelberg
HCI @ Uni Heidelberg
Computer Vision Group @ UC Berkeley
Institute for Machine Learning @ ETH Zurich