World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
34
Citations
20446
World Ranking
11864
National Ranking
584

Overview

Björn Ommer is affiliated with Ludwig-Maximilians-Universität München in Germany. Their primary field of study is Computer Science, with a focus on several subfields including Computer Vision and Pattern Recognition, Artificial Intelligence, Computer Graphics and Computer-Aided Design, Radiology, Nuclear Medicine and Imaging, and Computational Mechanics.

The main research topics covered by Ommer span Generative Adversarial Networks and Image Synthesis, Computer Graphics and Visualization Techniques, Advanced Vision and Imaging, Domain Adaptation and Few-Shot Learning, Advanced Image Processing Techniques, Multimodal Machine Learning Applications, and Medical Imaging Techniques and Applications.

Their recent published papers include:

  • High-Resolution Image Synthesis with Latent Diffusion Models, 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • High-Resolution Image Synthesis with Latent Diffusion Models, 2021, arXiv (Cornell University)
  • Neuropathic pain caused by miswiring and abnormal end organ targeting, 2022, Nature
  • SLIM: Self-Supervised LiDAR Scene Flow and Motion Segmentation, 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
  • State of the Art on Diffusion Models for Visual Computing, 2024, Computer Graphics Forum

The scientist has frequently published in the following venues:

  • arXiv (Cornell University)
  • 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • PLoS ONE
  • Medical Physics

Ommer has collaborated regularly with several co-authors, including:

  • Vincent Tao Hu
  • Robin Rombach
  • Patrick Esser
  • Pingchuan Ma
  • Stefan Andreas Baumann

Best Publications

  • High-Resolution Image Synthesis with Latent Diffusion Models

    Unknown

  • Taming Transformers for High-Resolution Image Synthesis

    Patrick Esser;Robin Rombach;Bjorn Ommer

  • High-Resolution Image Synthesis with Latent Diffusion Models

    Unknown

  • A Style-Aware Content Loss for Real-Time HD Style Transfer

    Artsiom Sanakoyeu;Dmytro Kotovenko;Sabine Lang;Björn Ommer

  • A Variational U-Net for Conditional Appearance and Shape Generation

    Patrick Esser;Ekaterina Sutter;Björn Ommer

  • Video parsing for abnormality detection

    Borislav Antic;Bjorn Ommer

  • Content and Style Disentanglement for Artistic Style Transfer

    Dmytro Kotovenko;Artsiom Sanakoyeu;Sabine Lang;Bjorn Ommer

  • Unsupervised Part-Based Disentangling of Object Shape and Appearance

    Dominik Lorenz;Leonard Bereska;Timo Milbich;Bjorn Ommer

  • Learning Multi-Scale Photo Exposure Correction

    Mahmoud Afifi;Konstantinos G. Derpanis;Bjorn Ommer;Michael S. Brown

  • Improving Spatiotemporal Self-supervision by Deep Reinforcement Learning

    Uta Büchler;Biagio Brattoli;Björn Ommer

  • Divide and Conquer the Embedding Space for Metric Learning

    Artsiom Sanakoyeu;Vadim Tschernezki;Uta Buchler;Bjorn Ommer

  • Taming Transformers for High-Resolution Image Synthesis

    Patrick Esser;Robin Rombach;Björn Ommer

  • Revisiting Training Strategies and Generalization Performance in Deep Metric Learning

    Karsten Roth;Timo Milbich;Samrath Sinha;Prateek Gupta

  • Multi-scale object detection by clustering lines

    Bjorn Ommer;Jitendra Malik

  • Learning the Compositional Nature of Visual Object Categories for Recognition

    B. Ommer;J.M. Buhmann

  • Deep Semantic Feature Matching

    Nikolai Ufer;Bjorn Ommer

  • MIC: Mining Interclass Characteristics for Improved Metric Learning

    Biagio Brattoli;Karsten Roth;Bjorn Ommer

  • CliqueCNN: Deep Unsupervised Exemplar Learning

    Miguel Ángel Bautista;Artsiom Sanakoyeu;Ekaterina Tikhoncheva;Björn Ommer

  • A Content Transformation Block for Image Style Transfer

    Dmytro Kotovenko;Artsiom Sanakoyeu;Pingchuan Ma;Sabine Lang

  • Cross and Learn: Cross-Modal Self-supervision

    Nawid Sayed;Biagio Brattoli;Björn Ommer

  • Learning the Compositional Nature of Visual Objects

    B. Ommer;J.M. Buhmann

  • State of the Art on Diffusion Models for Visual Computing

    Unknown

  • Retrieval-Augmented Diffusion Models

    Unknown

  • SLIM: Self-Supervised LiDAR Scene Flow and Motion Segmentation

    Stefan Andreas Baur;David Josef Emmerichs;Frank Moosmann;Peter Pinggera

  • A Disentangling Invertible Interpretation Network for Explaining Latent Representations

    Patrick Esser;Robin Rombach;Bjorn Ommer

  • Voting by grouping dependent parts

    Pradeep Yarlagadda;Antonio Monroy;Björn Ommer

  • MIC: Mining Interclass Characteristics for Improved Metric Learning

    Karsten Roth;Biagio Brattoli;Björn Ommer

Frequent Co-Authors

Martin E. Schwab
Martin E. Schwab University of Zurich
Fritjof Helmchen
Fritjof Helmchen University of Zurich
Yoshua Bengio
Yoshua Bengio University of Montreal
Michael S. Brown
Michael S. Brown York University
Volker Roth
Volker Roth University of Basel
Majid Mirmehdi
Majid Mirmehdi University of Bristol
Fred A. Hamprecht
Fred A. Hamprecht Heidelberg University
Jitendra Malik
Jitendra Malik University of California, Berkeley

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