Brief C.V.

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

• 2 papers accepted at NeurIPS'21 on:
  • Multinomial Diffusion for Improving Autoregressive Image Synthesis
  • Analyzing OOD Generalization in Deep Metric Learning
• T-PAMI article accepted on improving Deep Metric Learning by divide and conquer
• Nature Machine Intelligence article on unsupervised behavior analysis & magnification (uBAM) for biomedical diagnostics
• 3 papers accepted at ICCV'21 on:
  • Transformers for Geometry-Free 3D Novel-View Synthesis
  • iPOKE: Poking a Still Image for Controlled Stochastic Video Synthesis
  • Self-Supervised LiDAR Scene Flow and Motion Segmentation (ORAL)
• ICML'21 paper accepted on self-distillation for deep metric learning
• 6 papers accepted at CVPR'21 on:
  • Taming Transformers for Hi-Res Image Synthesis (aka. #VQGAN ), ORAL
  • Understanding Object Dynamics for Interactive Video Synthesis
  • Stochastic Image-to-Video Synthesis
  • Stroke-based Style Transfer
  • Behavior-driven Synthesis of Human Dynamics
  • Learning Exposure Correction
• Best Paper Award at CVPR'21—AI for Content Creation WS on
  • High-Res Complex Scene Synthesis with Transformers
• ICRA'21 paper accepted on 3D object detection
• 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

2018

Esser, P, Sutter, E and Ommer, B (2018). A Variational U-Net for Conditional Appearance and Shape Generation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (short Oral). https://compvis.github.io/vunet/ Google Scholar BibTeX RTF EndNote XML

Ghori, O, Mackowiak, R, Bautista, M, Beuter, N, Drumond, L, Diego, F and Ommer, B (2018). Learning to Forecast Pedestrian Intention from Pose Dynamics. Intelligent Vehicles, IEEE, 2018 Google Scholar BibTeX RTF EndNote XML

Büchler, U, Brattoli, B and Ommer, B (2018). Improving Spatiotemporal Self-Supervision by Deep Reinforcement Learning. Proceedings of the European Conference on Computer Vision (ECCV). (UB and BB contributed equally), Munich, Germany Google Scholar BibTeX RTF EndNote XML Article (5.34 MB) buechler_eccv18_poster.pdf (1.65 MB)

Sanakoyeu, A, Kotovenko, D, Lang, S and Ommer, B (2018). A Style-Aware Content Loss for Real-time HD Style Transfer. Proceedings of the European Conference on Computer Vision (ECCV) (Oral) Google Scholar BibTeX RTF EndNote XML

Sayed, N, Brattoli, B and Ommer, B (2018). Cross and Learn: Cross-Modal Self-Supervision. German Conference on Pattern Recognition (GCPR) (Oral). Stuttgart, Germany. https://arxiv.org/abs/1811.03879v1 Google Scholar BibTeX RTF EndNote XML Article (891.47 KB) Oral slides (9.17 MB)

Blum, O, Brattoli, B and Ommer, B (2018). X-GAN: Improving Generative Adversarial Networks with ConveX Combinations. German Conference on Pattern Recognition (GCPR) (Oral). Stuttgart, Germany Google Scholar BibTeX RTF EndNote XML Article (6.65 MB) Supplementary material (7.96 MB) Oral slides (14.96 MB)

Esser, P, Haux, J, Milbich, T and Ommer, B (2018). Towards Learning a Realistic Rendering of Human Behavior. European Conference on Computer Vision (ECCV - HBUGEN) Google Scholar BibTeX RTF EndNote XML

Lang, S and Ommer, B (2018). Reflecting on How Artworks Are Processed and Analyzed by Computer Vision. European Conference on Computer Vision (ECCV - VISART). Springer Google Scholar BibTeX RTF EndNote XML

Sanakoyeu, A, Bautista, M and Ommer, B (2018). Deep Unsupervised Learning of Visual Similarities. Pattern Recognition. 78. https://authors.elsevier.com/a/1WXUt77nKSb25 DOI Google Scholar BibTeX RTF EndNote XML PDF (8.35 MB)

Lang, S and Ommer, B (2018). Attesting Similarity: Supporting the Organization and Study of Art Image Collections with Computer Vision. Digital Scholarship in the Humanities, Oxford University Press. 33 845-856 Google Scholar BibTeX RTF EndNote XML

Wahl, A - S, Erlebach, E, Brattoli, B, Büchler, U, Kaiser, J, Ineichen, V B, Mosberger, A C, Schneeberger, S, Imobersteg, S, Wieckhorst, M, Stirn, M, Schroeter, A, Ommer, B and Schwab, M E (2018). Early reduced behavioral activity induced by large strokes affects the efficiency of enriched environment in rats. Sage Journals. Journal of Cerebral Blood Flow & Metabolism. http://journals.sagepub.com/doi/abs/10.1177/0271678X18777661 Google Scholar BibTeX RTF EndNote XML 0271678x18777661.pdf (770.87 KB)

Lang, S and Ommer, B (2018). Reconstructing Histories: Analyzing Exhibition Photographs with Computational Methods. Arts, Computational Aesthetics. 7, 64 Google Scholar BibTeX RTF EndNote XML arts-07-00064.pdf (4.6 MB)

2017

Ufer, N and Ommer, B (2017). Deep Semantic Feature Matching. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Google Scholar BibTeX RTF EndNote XML article (8.88 MB)

Brattoli, B, Büchler, U, Wahl, A - S, Schwab, M E and Ommer, B (2017). LSTM Self-Supervision for Detailed Behavior Analysis. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). (BB and UB contributed equally) Google Scholar BibTeX RTF EndNote XML Article (8.75 MB)

Milbich, T, Bautista, M, Sutter, E and Ommer, B (2017). Unsupervised Video Understanding by Reconciliation of Posture Similarities. Proceedings of the IEEE International Conference on Computer Vision (ICCV). https://hciweb.iwr.uni-heidelberg.de/compvis/research/tmilbich_iccv17 Google Scholar BibTeX RTF EndNote XML

Sümer, Ö, Dencker, T and Ommer, B (2017). Self-supervised Learning of Pose Embeddings from Spatiotemporal Relations in Videos. Proceedings of the IEEE International Conference on Computer Vision (ICCV) Google Scholar BibTeX RTF EndNote XML Paper (3.98 MB) Supplementary Material (3.36 MB)

Bautista, M, Sanakoyeu, A and Ommer, B (2017). Deep Unsupervised Similarity Learning using Partially Ordered Sets. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Google Scholar BibTeX RTF EndNote XML deep_unsupervised_similarity_learning_cvpr_2017_paper.pdf (905.82 KB)

Bautista, M, Fuchs, P and Ommer, B (2017). Learning Where to Drive by Watching Others. Proceedings of the German Conference Pattern Recognition. Springer-Verlag, Basel. 1 Google Scholar BibTeX RTF EndNote XML

Wahl, A - S, Büchler, U, Brändli, A, Brattoli, B, Musall, S, Kasper, H, Ineichen, B V, Helmchen, F, Ommer, B and Schwab, M E (2017). Optogenetically stimulating the intact corticospinal tract post-stroke restores motor control through regionalized functional circuit formation. Nature Communications. (ASW & UB contributed equally; BO and MES contributed equally). https://www.nature.com/articles/s41467-017-01090-6 DOI Google Scholar BibTeX RTF EndNote XML

2016

Bell, P and Ommer, B (2016). Digital Connoisseur? How Computer Vision Supports Art History. Connoisseurship nel XXI secolo. Approcci, Limiti, Prospettive, A. Aggujaro & S. Albl (ed.). Artemide, Rome Google Scholar BibTeX RTF EndNote XML

Bautista, M, Sanakoyeu, A, Sutter, E and Ommer, B (2016). CliqueCNN: Deep Unsupervised Exemplar Learning. Proceedings of the Conference on Advances in Neural Information Processing Systems (NIPS). MIT Press, Barcelona. https://arxiv.org/abs/1608.08792 Google Scholar BibTeX RTF EndNote XML Article (5.79 MB)

2015

Antic, B, Büchler, U, Wahl, A - S, Schwab, M E and Ommer, B (2015). Spatiotemporal Parsing of Motor Kinematics for Assessing Stroke Recovery. Medical Image Computing and Computer-Assisted Intervention. Springer Google Scholar BibTeX RTF EndNote XML Article (2.24 MB)

Rubio, J C and Ommer, B (2015). Regularizing Max-Margin Exemplars by Reconstruction and Generative Models. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. IEEE. 4213--4221 Google Scholar BibTeX RTF EndNote XML Technical Report (2.8 MB)

Antic, B and Ommer, B (2015). Per-Sample Kernel Adaptation for Visual Recognition and Grouping. Proceedings of the IEEE International Conference on Computer Vision. IEEE Google Scholar BibTeX RTF EndNote XML Technical Report (1.58 MB)

Rubio, J C, Eigenstetter, A and Ommer, B (2015). Generative Regularization with Latent Topics for Discriminative Object Recognition. Pattern Recognition. Elsevier. 48 3871--3880 Google Scholar BibTeX RTF EndNote XML Technical Report (5.49 MB)

Yarlagadda, P and Ommer, B (2015). Beyond the Sum of Parts: Voting with Groups of Dependent Entities. IEEE Transactions on Pattern Analysis and Machine Intelligence. IEEE. 37 1134--1147. https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6926849 Google Scholar BibTeX RTF EndNote XML

Bell, P and Ommer, B (2015). Training Argus. Kunstchronik. Monatsschrift für Kunstwissenschaft, Museumswesen und Denkmalpflege. Zentralinstitut für Kunstgeschichte. 68 414--420 Google Scholar BibTeX RTF EndNote XML

Antic, B and Ommer, B (2015). Spatio-temporal Video Parsing for Abnormality Detection. arXiv. abs/1502.06235. http://arxiv.org/abs/1502.06235 Google Scholar BibTeX RTF EndNote XML Technical Report (4.61 MB)

2014

Takami, M, Bell, P and Ommer, B (2014). An Approach to Large Scale Interactive Retrieval of Cultural Heritage. Eurographics Workshop on Graphics and Cultural Heritage. The Eurographics Association Google Scholar BibTeX RTF EndNote XML Technical Report (7.94 MB)

Takami, M, Bell, P and Ommer, B (2014). Offline Learning of Prototypical Negatives for Efficient Online Exemplar SVM. Winter Conference on Applications of Computer Vision. IEEE. 377--384. http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6836075 Google Scholar BibTeX RTF EndNote XML

Kandemir, M, Rubio, J C, Schmidt, U, Wojek, C, Welbl, J, Ommer, B and Hamprecht, F A (2014). Event Detection by Feature Unpredictability in Phase-Contrast Videos of Cell Cultures. Medical Image Computing and Computer-Assisted Intervention. Springer. 154--161 Google Scholar BibTeX RTF EndNote XML Technical Report (2 MB)

Antic, B and Ommer, B (2014). Learning Latent Constituents for Recognition of Group Activities in Video. Proceedings of the European Conference on Computer Vision (ECCV) (Oral). Springer. 33--47 Google Scholar BibTeX RTF EndNote XML Technical Report (4.54 MB)

Eigenstetter, A, Takami, M and Ommer, B (2014). Randomized Max-Margin Compositions for Visual Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. IEEE. 3590--3597 Google Scholar BibTeX RTF EndNote XML Technical Report (8.01 MB)

Kandemir, M, Rubio, J C, Schmidt, U, Welbl, J, Ommer, B and Hamprecht, F A (2014). Event Detection by Feature Unpredictability in Phase-Contrast Videos of Cell Cultures. MICCAI. Proceedings. Springer. 154-161 DOI Google Scholar BibTeX RTF EndNote XML Paper (2 MB)

Monroy, A, Bell, P and Ommer, B (2014). Morphological Analysis for Investigating Artistic Images. Image and Vision Computing. Elsevier. 32 414--423 Google Scholar BibTeX RTF EndNote XML Technical Report (2.86 MB)

Wahl, A - S, Omlor, W, Rubio, J C, Chen, J L, Zheng, H, Schröter, A, Gullo, M, Weinmann, O, Kobayashi, K, Helmchen, F, Ommer, B and Schwab, M E (2014). Asynchronous Therapy Restores Motor Control by Rewiring of the Rat Corticospinal Tract after Stroke. Science. American Association for The Advancement of Science. 344 1250--1255. http://www.sciencemag.org/content/344/6189/1250 Google Scholar BibTeX RTF EndNote XML

2013

Ommer, B (2013). The Role of Shape in Visual Recognition. Shape Perception in Human Computer Vision: An Interdisciplinary Perspective. Springer. 373--385 Google Scholar BibTeX RTF EndNote XML Technical Report (8.18 MB)

Garbe, C S and Ommer, B (2013). Parameter Estimation in Image Processing and Computer Vision. Model Based Parameter Estimation: Theory and Applications. Springer. 311--334 Google Scholar BibTeX RTF EndNote XML Technical Report (928 KB)

Yarlagadda, P, Monroy, A, Carque, B and Ommer, B (2013). Towards a Computer-based Understanding of Medieval Images. Scientific Computing & Cultural Heritage. Springer. 89--97. http://link.springer.com/chapter/10.1007/978-3-642-28021-4_10 Google Scholar BibTeX RTF EndNote XML

Antic, B, Milbich, T and Ommer, B (2013). Less is More: Video Trimming for Action Recognition. Proceedings of the IEEE International Conference on Computer Vision, Workshop on Understanding Human Activities: Context and Interaction. IEEE. 515--521 Google Scholar BibTeX RTF EndNote XML Technical Report (984.89 KB)

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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

University of Heidelberg

Computer Vision Group @ UC Berkeley

Institute for Machine Learning @ ETH Zurich