World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
39
Citations
11329
World Ranking
9523
National Ranking
471

Overview

Laura Leal-Taixé is a researcher affiliated with the Technical University of Munich in Germany. Their work primarily focuses on the field of Computer Science, with a significant emphasis on Computer Vision and Pattern Recognition.

The scientist's research spans several subfields, including:

  • Computer Vision and Pattern Recognition
  • Artificial Intelligence
  • Aerospace Engineering
  • Automotive Engineering
  • Atmospheric Science

The main topics addressed in their publications cover a broad range of applications and methodologies:

  • Video Surveillance and Tracking Methods
  • Advanced Neural Network Applications
  • Human Pose and Action Recognition
  • Advanced Image and Video Retrieval Techniques
  • Domain Adaptation and Few-Shot Learning
  • Robotics and Sensor-Based Localization
  • Anomaly Detection Techniques and Applications

Key recent papers by Laura Leal-Taixé include:

  • TrackFormer: Multi-Object Tracking with Transformers, 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • MOT20: A benchmark for multi object tracking in crowded scenes, 2020, arXiv (Cornell University)
  • MG-GAN: A Multi-Generator Model Preventing Out-of-Distribution Samples in Pedestrian Trajectory Prediction, 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
  • DynamicEarthNet: Daily Multi-Spectral Satellite Dataset for Semantic Change Segmentation, 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • The Center of Attention: Center-Keypoint Grouping via Attention for Multi-Person Pose Estimation, 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV)

Their frequent collaborators include the following researchers:

  • Aljoša Ošep
  • Daniel Cremers
  • Guillem Brasó
  • Tim Meinhardt
  • Ismail Elezi

The main publication venues for their work feature a combination of conferences and repositories:

  • arXiv (Cornell University)
  • 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
  • Astronomy and Astrophysics
  • International Journal of Computer Vision

Best Publications

  • MOT16: A Benchmark for Multi-Object Tracking

    Anton Milan;Laura Leal-Taixé;Ian D. Reid;Stefan Roth

  • HOTA: A Higher Order Metric for Evaluating Multi-object Tracking.

    Jonathon Luiten;Aljosa Osep;Patrick Dendorfer;Philip H. S. Torr

  • One-Shot Video Object Segmentation

    S. Caelles;K. K. Maninis;J. Pont-Tuset;L. Leal-Taixe

  • TrackFormer: Multi-Object Tracking with Transformers.

    Tim Meinhardt;Alexander Kirillov;Laura Leal-Taixé;Christoph Feichtenhofer

  • MOTChallenge 2015: Towards a Benchmark for Multi-Target Tracking

    Laura Leal-Taixé;Anton Milan;Ian D. Reid;Stefan Roth

  • Tracking Without Bells and Whistles

    Philipp Bergmann;Tim Meinhardt;Laura Leal-Taixe

  • Image-Based Localization Using LSTMs for Structured Feature Correlation

    F. Walch;C. Hazirbas;L. Leal-Taixe;T. Sattler

  • MOT20: A benchmark for multi object tracking in crowded scenes.

    Patrick Dendorfer;Hamid Rezatofighi;Anton Milan;Javen Shi

  • Learning by Tracking: Siamese CNN for Robust Target Association

    Laura Leal-Taixe;Cristian Canton-Ferrer;Konrad Schindler

  • Understanding the Limitations of CNN-Based Absolute Camera Pose Regression

    Torsten Sattler;Qunjie Zhou;Marc Pollefeys;Laura Leal-Taixe

  • Learning a Neural Solver for Multiple Object Tracking

    Guillem Braso;Laura Leal-Taixe

  • Video Object Segmentation without Temporal Information

    K.-K. Maninis;S. Caelles;Y. Chen;J. Pont-Tuset

  • Learning an Image-Based Motion Context for Multiple People Tracking

    Laura Leal-Taixé;Michele Fenzi;Alina Kuznetsova;Bodo Rosenhahn

  • Everybody needs somebody: Modeling social and grouping behavior on a linear programming multiple people tracker

    Laura Leal-Taixe;Gerard Pons-Moll;Bodo Rosenhahn

  • MOTChallenge: A Benchmark for Single-Camera Multiple Target Tracking

    Patrick Dendorfer;Aljosa Osep;Anton Milan;Konrad Schindler

  • Patch2Pix: Epipolar-Guided Pixel-Level Correspondences

    Qunjie Zhou;Torsten Sattler;Laura Leal-Taixe

  • How to Train Your Deep Multi-Object Tracker

    Yihong Xu;Aljosa sep;Yutong Ban;Radu Horaud

  • Joint tracking and segmentation of multiple targets

    Anton Milan;Laura Leal-Taixe;Konrad Schindler;Ian Reid

  • EagerMOT: 3D Multi-Object Tracking via Sensor Fusion

    Aleksandr Kim;Aljosa Osep;Laura Leal-Taixe

  • Real-Time Sign Language Recognition Using a Consumer Depth Camera

    Alina Kuznetsova;Laura Leal-Taixe;Bodo Rosenhahn

  • CIAGAN: Conditional Identity Anonymization Generative Adversarial Networks

    Maxim Maximov;Ismail Elezi;Laura Leal-Taixe

  • Fusion of Head and Full-Body Detectors for Multi-object Tracking

    Roberto Henschel;Laura Leal-Taixe;Daniel Cremers;Bodo Rosenhahn

  • Tracking the Trackers: An Analysis of the State of the Art in Multiple Object Tracking

    Laura Leal-Taixé;Anton Milan;Konrad Schindler;Daniel Cremers

  • DynamicEarthNet: Daily Multi-Spectral Satellite Dataset for Semantic Change Segmentation

    Unknown

  • STEm-Seg: Spatio-Temporal Embeddings for Instance Segmentation in Videos

    Ali Athar;Sabarinath Mahadevan;Aljos̆a Os̆ep;Laura Leal-Taixé

  • MG-GAN: A Multi-Generator Model Preventing Out-of-Distribution Samples in Pedestrian Trajectory Prediction

    Patrick Dendorfer;Sven Elflein;Laura Leal-Taixé

  • MG-GAN: A Multi-Generator Model Preventing Out-of-Distribution Samples in Pedestrian Trajectory Prediction

    Patrick Dendorfer;Sven Elflein;Laura Leal-Taixé

  • Outdoor human motion capture using inverse kinematics and von mises-fisher sampling

    Gerard Pons-Moll;Andreas Baak;Juergen Gall;Laura Leal-Taixe

Frequent Co-Authors

Daniel Cremers
Daniel Cremers Technical University of Munich
Bodo Rosenhahn
Bodo Rosenhahn University of Hannover
Ian Reid
Ian Reid University of Adelaide
Bastian Leibe
Bastian Leibe RWTH Aachen University
Torsten Sattler
Torsten Sattler Czech Technical University in Prague
Stefan Roth
Stefan Roth Technical University of Darmstadt
Gerard Pons-Moll
Gerard Pons-Moll University of Tübingen
Marc Pollefeys
Marc Pollefeys ETH Zurich
Andreas Geiger
Andreas Geiger University of Tübingen

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

Pursuing a computer science degree in the USA opens doors to a range of related fields and online learning opportunities. Many students consider accelerating their studies to start their tech careers sooner—explore the accelerated computer science degree options available online for faster completion.

If you’re interested in expanding your expertise, consider disciplines that closely align with computer science. A background in computing can also help answer the question, what can you get with an environmental science degree, by enabling you to apply technology toward solving environmental problems.

Engineering is another popular pathway. You might be interested in obtaining an online environmental engineering degree or an online degree in mechanical engineering, both of which frequently overlap with computer science in areas like automation and data analysis.

Exploring these degrees and career paths can broaden your opportunities and let you specialize in growing industries at the intersection of technology and innovation.

Best Scientists Citing Laura Leal-Taixé

Trending Scientists

Recently Published Articles