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

• Nature Machine Intelligence article on unsupervised behavior analysis & magnification (uBAM) for biomedical diagnostics
• 6 papers accepted at CVPR'21 on:
  • Taming Transformers for Hi-Res Image Synthesis
  • Learning Object Dynamics for Interactive Video Synthesis
  • Stochastic Image-to-Video Synthesis
  • Stroke-based Style Transfer
  • Behavior-driven Synthesis of Human Dynamics
  • Learning Exposure Correction
• 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

2021

Afifi, M, Derpanis, K G, Ommer, B and Brown, M S (2021). Learning Multi-Scale Photo Exposure Correction. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). https://arxiv.org/abs/2003.11596 Google Scholar BibTeX RTF EndNote XML

Blattmann, A, Milbich, T, Dorkenwald, M and Ommer, B (2021). Behavior-Driven Synthesis of Human Dynamics. CVPR2021. https://arxiv.org/abs/2103.04677 Google Scholar BibTeX RTF EndNote XML

Kotovenko, D, Wright, M, Heimbrecht, A and Ommer, B (2021). Rethinking Style Transfer: From Pixels to Parameterized Brushstrokes. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). https://arxiv.org/abs/2103.17185 Google Scholar BibTeX RTF EndNote XML

Islam, M Amirul, Kowal, M, Esser, P, Jia, S, Ommer, B, Derpanis, K G and Bruce, N (2021). Shape or Texture: Understanding Discriminative Features in CNNs. International Conference on Learning Representations (ICLR) Google Scholar BibTeX RTF EndNote XML

Esser, P, Rombach, R and Ommer, B (2021). Taming Transformers for High-Resolution Image Synthesis. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). https://arxiv.org/abs/2012.09841 Google Scholar BibTeX RTF EndNote XML

Brattoli, B, Büchler, U, Dorkenwald, M, Reiser, P, Filli, L, Helmchen, F, Wahl, A - S and Ommer, B (2021). Unsupervised behaviour analysis and magnification (uBAM) using deep learning. Nature Machine Intelligence. https://rdcu.be/ch6pL DOI Google Scholar BibTeX RTF EndNote XML

2020

Esser, P, Rombach, R and Ommer, B (2020). A Note on Data Biases in Generative Models. NeurIPS 2020 Workshop on Machine Learning for Creativity and Design. https://arxiv.org/abs/2012.02516 Google Scholar BibTeX RTF EndNote XML

Rombach, R, Esser, P and Ommer, B (2020). Network Fusion for Content Creation with Conditional INNs. CVPRW 2020 (AI for Content Creation). https://compvis.github.io/network-fusion/ Google Scholar BibTeX RTF EndNote XML

Milbich, T, Roth, K and Ommer, B (2020). PADS: Policy-Adapted Sampling for Visual Similarity Learning. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 1. https://arxiv.org/abs/2003.11113 Google Scholar BibTeX RTF EndNote XML

Dorkenwald, M, Büchler, U and Ommer, B (2020). Unsupervised Magnification of Posture Deviations Across Subjects. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Google Scholar BibTeX RTF EndNote XML article.pdf (1.15 MB)

Lang, S and Ommer, B (2020). Das Objekt jenseits der Digitalisierung. Das digitale Objekt. 7. http://www.deutsches-museum.de/fileadmin/Content/010_DM/060_Verlag/studies-7.pdf Google Scholar BibTeX RTF EndNote XML lang_ommer_digitalhumanities_2020_.pdf (599.56 KB)

Rombach, R, Esser, P and Ommer, B (2020). Network-to-Network Translation with Conditional Invertible Neural Networks. Neural Information Processing Systems (NeurIPS) (Oral). https://compvis.github.io/net2net/ Google Scholar BibTeX RTF EndNote XML

Braun, S, Esser, P and Ommer, B (2020). Unsupervised Part Discovery by Unsupervised Disentanglement. Proceedings of the German Conference on Pattern Recognition (GCPR) (Oral). Tübingen. https://compvis.github.io/unsupervised-part-segmentation/ Google Scholar BibTeX RTF EndNote XML

Rombach, R, Esser, P and Ommer, B (2020). Making Sense of CNNs: Interpreting Deep Representations & Their Invariances with INNs. IEEE European Conference on Computer Vision (ECCV). https://compvis.github.io/invariances/ Google Scholar BibTeX RTF EndNote XML

Milbich, T, Roth, K, Bharadhwaj, H, Sinha, S, Bengio, Y, Ommer, B and Cohen, J Paul (2020). DiVA: Diverse Visual Feature Aggregation for Deep Metric Learning. IEEE European Conference on Computer Vision (ECCV). https://arxiv.org/abs/2004.13458 Google Scholar BibTeX RTF EndNote XML

Esser, P, Rombach, R and Ommer, B (2020). A Disentangling Invertible Interpretation Network for Explaining Latent Representations. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). https://compvis.github.io/iin/ Google Scholar BibTeX RTF EndNote XML Article (13.07 MB)

Roth, K, Milbich, T, Sinha, S, Gupta, P, Ommer, B and Cohen, J Paul (2020). Revisiting Training Strategies and Generalization Performance in Deep Metric Learning. International Conference on Machine Learning (ICML). https://arxiv.org/pdf/2002.08473.pdf Google Scholar BibTeX RTF EndNote XML

Dencker, T, Klinkisch, P, Maul, S M and Ommer, B (2020). Deep learning of cuneiform sign detection with weak supervision using transliteration alignment. PLoS ONE. 15. https://hci.iwr.uni-heidelberg.de/compvis/projects/cuneiform DOI Google Scholar BibTeX RTF EndNote XML

Milbich, T, Roth, K, Brattoli, B and Ommer, B (2020). Sharing Matters for Generalization in Deep Metric Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI). https://arxiv.org/abs/2004.05582 DOI Google Scholar BibTeX RTF EndNote XML

Milbich, T, Ghori, O and Ommer, B (2020). Unsupervised Representation Learning by Discovering Reliable Image Relations. Pattern Recognition. 102. http://arxiv.org/abs/1911.07808 Google Scholar BibTeX RTF EndNote XML

2019

Lorenz, D, Bereska, L, Milbich, T and Ommer, B (2019). Unsupervised Part-Based Disentangling of Object Shape and Appearance. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (Oral + Best paper finalist: top 45 / 5160 submissions) Google Scholar BibTeX RTF EndNote XML

Brattoli, B, Roth, K and Ommer, B (2019). MIC: Mining Interclass Characteristics for Improved Metric Learning. Proceedings of the Intl. Conf. on Computer Vision (ICCV) Google Scholar BibTeX RTF EndNote XML

Kotovenko, D, Sanakoyeu, A, Lang, S and Ommer, B (2019). Content and Style Disentanglement for Artistic Style Transfer. Proceedings of the Intl. Conf. on Computer Vision (ICCV) Google Scholar BibTeX RTF EndNote XML

Ufer, N, Lui, K To, Schwarz, K, Warkentin, P and Ommer, B (2019). Weakly Supervised Learning of Dense SemanticCorrespondences and Segmentation. German Conference on Pattern Recognition (GCPR) Google Scholar BibTeX RTF EndNote XML article (6.1 MB)

Esser, P, Haux, J and Ommer, B (2019). Unsupervised Robust Disentangling of Latent Characteristics for Image Synthesis. Proceedings of the Intl. Conf. on Computer Vision (ICCV). https://compvis.github.io/robust-disentangling/ Google Scholar BibTeX RTF EndNote XML

Kotovenko, D, Sanakoyeu, A, Lang, S, Ma, P and Ommer, B (2019). Using a Transformation Content Block For Image Style Transfer. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Google Scholar BibTeX RTF EndNote XML

Sanakoyeu, A, Tschernezki, V, Büchler, U and Ommer, B (2019). Divide and Conquer the Embedding Space for Metric Learning. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). https://github.com/CompVis/metric-learning-divide-and-conquer Google Scholar BibTeX RTF EndNote XML

2018

Bell, P and Ommer, B (2018). Computer Vision und Kunstgeschichte — Dialog zweier Bildwissenschaften. Computing Art Reader: Einführung in die digitale Kunstgeschichte, P. Kuroczyński et al. (ed.) Google Scholar BibTeX RTF EndNote XML 413-17-83318-2-10-20181210.pdf (2.98 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

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)

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)

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

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)

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

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

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

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)

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

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)

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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