World's Best Scientists 2026 revealed!
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Computer Science
Germany
2025

D-Index & Metrics

Computer Science

D-Index
105
Citations
153185
World Ranking
278
National Ranking
14

Research.com Recognitions

  • 2025 - Research.com Computer Science in Germany Leader Award
  • 2023 - Research.com Computer Science in Germany Leader Award
  • 2022 - Research.com Computer Science in Germany Leader Award

Overview

Thomas Brox is affiliated with the University of Freiburg in Germany and focuses primarily on the field of Computer Science. Their research intersects multiple subfields, including Computer Vision and Pattern Recognition, Artificial Intelligence, Cognitive Neuroscience, Control and Systems Engineering, and Radiology, Nuclear Medicine and Imaging.

The scientist's work addresses a range of topics, notably Domain Adaptation and Few-Shot Learning, Advanced Neural Network Applications, Anomaly Detection Techniques and Applications, Multimodal Machine Learning Applications, Adversarial Robustness in Machine Learning, Advanced Image and Video Retrieval Techniques, and Human Pose and Action Recognition.

Among recent publications, the following papers represent key contributions:

  • Towards Total Recall in Industrial Anomaly Detection, 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • U-Prithvi: Integrating a Foundation Model and U-Net for Enhanced Flood Inundation Mapping, 2025, Dagstuhl Research Online Publication Server
  • CrossCLR: Cross-modal Contrastive Learning For Multi-modal Video Representations, 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
  • COOT: Cooperative Hierarchical Transformer for Video-Text Representation Learning, 2020, arXiv (Cornell University)
  • Explainable AI for CNN-based prostate tumor segmentation in multi-parametric MRI correlated to whole mount histopathology, 2022, Radiation Oncology

Thomas Brox has frequently collaborated with several researchers, including Andrea Vedaldi, Horst Bischof, Jan-Michael Frahm, Max Argus, and Simon Schrodi. Each of these coauthors has contributed to multiple joint publications.

Publication venues commonly hosting their work include arXiv (Cornell University), bioRxiv (Cold Spring Harbor Laboratory), 2021 IEEE/CVF International Conference on Computer Vision (ICCV), Nature Communications, and Neuron.

The scientist has also contributed extensively to book publications, particularly with Springer Science+Business Media. Notable among these is the volume titled "Computer Vision - ECCV 2020," which appeared in several editions within the year 2020.

Best Publications

  • U-Net: Convolutional Networks for Biomedical Image Segmentation

    Olaf Ronneberger;Philipp Fischer;Thomas Brox

  • 3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation

    Özgün Çiçek;Ahmed Abdulkadir;Ahmed Abdulkadir;Soeren S. Lienkamp;Thomas Brox

  • FlowNet: Learning Optical Flow with Convolutional Networks

    Alexey Dosovitskiy;Philipp Fischery;Eddy Ilg;Philip Hausser

  • High Accuracy Optical Flow Estimation Based on a Theory for Warping

    Thomas Brox;Andr ´ es Bruhn;Nils Papenberg;Joachim Weickert

  • Striving for Simplicity: The All Convolutional Net

    Jost Tobias Springenberg;Alexey Dosovitskiy;Thomas Brox;Martin A. Riedmiller

  • FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks

    Eddy Ilg;Nikolaus Mayer;Tonmoy Saikia;Margret Keuper

  • A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimation

    Nikolaus Mayer;Eddy Ilg;Philip Hausser;Philipp Fischer

  • U-Net: deep learning for cell counting, detection, and morphometry

    Thorsten Falk;Dominic Mai;Robert Bensch;Özgün Çiçek

  • Large Displacement Optical Flow: Descriptor Matching in Variational Motion Estimation

    T Brox;J Malik

  • Towards Total Recall in Industrial Anomaly Detection

    Karsten Roth;Latha Pemula;Joaquin Zepeda;Bernhard Schölkopf

  • FlowNet: Learning Optical Flow with Convolutional Networks

    Philipp Fischer;Alexey Dosovitskiy;Eddy Ilg;Philip Häusser

  • Discriminative Unsupervised Feature Learning with Convolutional Neural Networks

    Alexey Dosovitskiy;Jost Tobias Springenberg;Martin Riedmiller;Thomas Brox

  • Object segmentation by long term analysis of point trajectories

    Thomas Brox;Jitendra Malik

  • Generating Images with Perceptual Similarity Metrics based on Deep Networks

    Alexey Dosovitskiy;Thomas Brox

  • Sparsity Invariant CNNs

    Jonas Uhrig;Nick Schneider;Lukas Schneider;Uwe Franke

  • Learning to generate chairs with convolutional neural networks

    Alexey Dosovitskiy;Jost Tobias Springenberg;Thomas Brox

  • Octree Generating Networks: Efficient Convolutional Architectures for High-resolution 3D Outputs

    Maxim Tatarchenko;Alexey Dosovitskiy;Thomas Brox

  • Discriminative Unsupervised Feature Learning with Exemplar Convolutional Neural Networks

    Alexey Dosovitskiy;Philipp Fischer;Jost Tobias Springenberg;Martin Riedmiller

  • Inverting Visual Representations with Convolutional Networks

    Alexey Dosovitskiy;Thomas Brox

  • DeMoN: Depth and Motion Network for Learning Monocular Stereo

    Benjamin Ummenhofer;Huizhong Zhou;Jonas Uhrig;Nikolaus Mayer

  • Learning to Estimate 3D Hand Pose from Single RGB Images

    Christian Zimmermann;Thomas Brox

Frequent Co-Authors

Alexey Dosovitskiy
Alexey Dosovitskiy Google (United States)
Daniel Cremers
Daniel Cremers Technical University of Munich
Bodo Rosenhahn
Bodo Rosenhahn University of Hannover
Joachim Weickert
Joachim Weickert Saarland University
Olaf Ronneberger
Olaf Ronneberger University of Freiburg
Wolfram Burgard
Wolfram Burgard University of Technology Nuremberg
Hans-Peter Seidel
Hans-Peter Seidel Max Planck Institute for Informatics
Bernt Schiele
Bernt Schiele Max Planck Institute for Informatics
Jost Tobias Springenberg
Jost Tobias Springenberg University of Freiburg
Uwe Franke
Uwe Franke Daimler (Germany)

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