Laura Leal-Taixé

Fellowship
Rudolf Mößbauer Tenure Track

Appointment
2017

Institution
Technical University of Munich

Department
Informatics

Focus Group/Research Project
Dynamic Vision and Learning

Scientific Report on TUM-IAS Fellowship

In the framework of the TUM-IAS Rudolf Mößbauer Tenure Track Assistant Professorship, the Professorship for Quantum Electronics and Computer Engineering was established in 2019 and promoted to W3 Associate Professorship in 2022. The group’s research focuses on quantum engineering of photonic quantum systems. full report …

Short CV

Prof. Dr.-Ing. Laura Leal-Taixé was born in Barcelona where she pursued B.Sc. and M.Sc. degrees in Telecommunications Engineering at the Technical University of Catalonia (UPC). She went to Boston, USA to do her Master’s thesis at Northeastern University with a fellowship from the Vodafone foundation. She worked on computer vision and image processing for medical images. From 2009 until 2013, she worked towards her PhD degree at the Institute for Information Processing (TNT) of the Leibniz University of Hannover in Germany. She focused on topics such as multiple people tracking, object detection, pose estimation. She graduated in February 2014, presenting the thesis “Multiple people tracking with context awareness”. During her PhD, she was at the University of Michigan, Ann Arbor, USA, for one year as a visiting scholar with Prof. Silvio Savarese. She then spent two years as a postdoctoral researcher at the Institute for Geodesy and Photogrammetry at ETH Zürich, Switzerland, working on tracking and benchmarking.

In May 2016, she moved to Munich where she started working at the Computer Vision Group as a senior postdoctoral researcher.

She is currently as Professor at the Dynamic Vision and Learning group after receiving a Sofja Kovalevskaja Award of 1.65 million euros for her project socialMaps.


Selected Awards

  • 2017, Sofja Kovalevskaja Award, 1.65 million euros, Humboldt Foundation
  • 2017, DAAD Funding, Australia-German Joint Research Corporation Scheme
  • 2016, Travel grant awarded by the Women in Computer Vision Association at CVPR
  • 2013, Travel grant awarded for the Doctoral Consortium at CVPR
  • 2008, Vodafone scholarship to pursue the Master Thesis in the USA

Research Interests

Prof. Dr.-Ing. Laura Leal-Taixé (b. 1984) conducts research in the area of Computer Vision and Machine Learning. In particular, she focuses on video analysis, solving tasks such as multiple object tracking, motion analysis or semantic segmentation. Allowing machines to automatically analyse video data is essential for applications such as Autonomous Driving.

During her doctoral studies, she focused on including social and multi-view information into optimization schemes for multiple pedestrian tracking. She later moved towards Machine Learning and how to better adapt learning algorithms such as neural networks to work on video streams.

In the project socialMaps, for which she has been granted a Sofja Kovalevskaja Award, she proposes to include dynamic and social information into static maps, so as to decouple vehicle traffic from pedestrian traffic.


Selected Publications

  • Fenzi, Michele; Leal-Taixe, Laura; Ostermann, Jorn; Tuytelaars, Tinne: Continuous Pose Estimation with a Spatial Ensemble of Fisher Regressors. 2015 IEEE International Conference on Computer Vision (ICCV), IEEE, 2015
  • Milan, Anton; Leal-Taixe, Laura; Schindler, Konrad; Reid, Ian: Joint tracking and segmentation of multiple targets. 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, 2015 
  • Leal-Taixe, Laura; Fenzi, Michele; Kuznetsova, Alina; Rosenhahn, Bodo; Savarese, Silvio: Learning an Image-Based Motion Context for Multiple People Tracking. 2014 IEEE Conference on Computer Vision and Pattern Recognition, IEEE, 2014 
  • Fenzi, Michele; Leal-Taixe, Laura; Rosenhahn, Bodo; Ostermann, Jorn: Class Generative Models Based on Feature Regression for Pose Estimation of Object Categories. 2013 IEEE Conference on Computer Vision and Pattern Recognition, IEEE, 2013
  • Leal-Taixe, Laura; Pons-Moll, G.; Rosenhahn, B.: Branch-and-price global optimization for multi-view multi-target tracking. 2012 IEEE Conference on Computer Vision and Pattern Recognition, IEEE, 2012 
  • Leal-Taixe, Laura; Pons-Moll, Gerard; Rosenhahn, Bodo: Everybody needs somebody: Modeling social and grouping behavior on a linear programming multiple people tracker. 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops), IEEE, 2011

Publications as TUM-IAS-Fellow

2022

  • Brasó, Guillem; Cetintas, Orcun; Leal-Taixé, Laura: Multi-Object Tracking and Segmentation Via Neural Message Passing. International Journal of Computer Vision 130 (12), 2022, 3035-3053 mehr… BibTeX Volltext ( DOI )
  • Dendorfer, Patrick; Yugay, Vladimir; Ošep, Aljoša; Leal-Taixé, Laura: Quo Vadis: Is Trajectory Forecasting the Key Towards Long-Term Multi-Object Tracking? , 2022 mehr… BibTeX Volltext ( DOI )
  • Eisenberger, Marvin; Toker, Aysim; Leal-Taixé, Laura; Bernard, Florian; Cremers, Daniel: A Unified Framework for Implicit Sinkhorn Differentiation. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 mehr… BibTeX
  • Elezi, Ismail; Seidenschwarz, Jenny; Wagner, Laurin; Vascon, Sebastiano; Torcinovich, Alessandro; Pelillo, Marcello; Leal-Taixe, Laura: The Group Loss++: A deeper look into group loss for deep metric learning. , 2022 mehr… BibTeX Volltext ( DOI )
  • Fomenko, Vladimir; Elezi, Ismail; Ramanan, Deva; Leal-Taixé, Laura; Ošep, Aljoša: Learning to Discover and Detect Objects. , 2022 mehr… BibTeX Volltext ( DOI )
  • Gladkova, Mariia; Korobov, Nikita; Demmel, Nikolaus; Ošep, Aljoša; Leal-Taixé, Laura; Cremers, Daniel: DirectTracker: 3D Multi-Object Tracking Using Direct Image Alignment and Photometric Bundle Adjustment. , 2022 mehr… BibTeX Volltext ( DOI )
  • Kim, Aleksandr; Brasó, Guillem; Ošep, Aljoša; Leal-Taixé, Laura: PolarMOT: How Far Can Geometric Relations Take Us in 3D Multi-Object Tracking? , 2022 mehr… BibTeX Volltext ( DOI )
  • Kocsis, Peter; Súkeník, Peter; Brasó, Guillem; Nießner, Matthias; Leal-Taixé, Laura; Elezi, Ismail: The Unreasonable Effectiveness of Fully-Connected Layers for Low-Data Regimes. , 2022 mehr… BibTeX Volltext ( DOI )
  • Kolmet Manuel, Zhou Qunjie,Osep Aljosa, and Leal-Taixe Laura : Towards cross-modal pose localization from text-based position descriptions. , 2022 mehr… BibTeX Volltext (mediaTUM)
  • Maximov Maxim,Elezi Ismail, and Leal-Taix Laura: Decoupling identity and visual quality for image and video anonymization. , 2022 mehr… BibTeX Volltext (mediaTUM)
  • Nunes, Lucas; Chen, Xieyuanli; Marcuzzi, Rodrigo; Osep, Aljosa; Leal-Taixé, Laura; Stachniss, Cyrill; Behley, Jens: Unsupervised Class-Agnostic Instance Segmentation of 3D LiDAR Data for Autonomous Vehicles. IEEE Robotics and Automation Letters 7 (4), 2022, 8713-8720 mehr… BibTeX Volltext ( DOI )
  • Peri, Neehar; Luiten, Jonathon; Li, Mengtian; Ošep, Aljoša; Leal-Taixé, Laura; Ramanan, Deva: Forecasting from LiDAR via Future Object Detection. , 2022 mehr… BibTeX Volltext ( DOI )
  • Pflugfelder, Roman; Weissenfeld, Axel; Wagner, Julian: Deep Vehicle Detection in Satellite Video. , 2022 mehr… BibTeX Volltext ( DOI )
  • Toker, Aysim; Kondmann, Lukas; Weber, Mark; Eisenberger, Marvin; Camero, Andrés; Hu, Jingliang; Hoderlein, Ariadna Pregel; Şenaras, Çağlar; Davis, Timothy; Cremers, Daniel; Marchisio, Giovanni; Zhu, Xiao Xiang; Leal-Taixé, Laura: DynamicEarthNet: Daily Multi-Spectral Satellite Dataset for Semantic Change Segmentation. , 2022 mehr… BibTeX Volltext ( DOI )
  • Zhou, Qunjie; Agostinho, Sérgio; Osep, Aljosa; Leal-Taixé, Laura: Is Geometry Enough for Matching in Visual Localization? , 2022 mehr… BibTeX Volltext ( DOI )

2021

  • Elezi, Ismail; Yu, Zhiding; Anandkumar, Anima; Leal-Taixe, Laura; Alvarez, Jose M.: Not All Labels Are Equal: Rationalizing The Labeling Costs for Training Object Detection. , 2021 mehr… BibTeX Volltext ( DOI )
  • Liu, Yang; Zulfikar, Idil Esen; Luiten, Jonathon; Dave, Achal; Ramanan, Deva; Leibe, Bastian; Ošep, Aljoša; Leal-Taixé, Laura: Opening up Open-World Tracking. , 2021 mehr… BibTeX Volltext ( DOI )
  • Meinhardt, Tim; Kirillov, Alexander; Leal-Taixe, Laura; Feichtenhofer, Christoph: TrackFormer: Multi-Object Tracking with Transformers. , 2021 mehr… BibTeX Volltext ( DOI )

2019

  • Ismail Elezi, Sebastiano Vascon, Alessandro Torcinovich, Marcello Pelillo, Laura Leal-Taixe: The Group Loss for Deep Metric Learning. 2019 mehr… BibTeX
  • Kishan Sharma, Moritz Gold, Christian Zurbruegg, Laura Leal-Taixé, Jan Dirk Wegner: HistoNet: Predicting size histograms of object instances. 2019 mehr… BibTeX
  • Maxim Maximov, Laura Leal-Taixé, Mario Fritz, Tobias Ritschel: Deep Appearance Maps. 2019 mehr… BibTeX
  • Patrick Dendorfer, Hamid Rezatofighi, Anton Milan, Javen Shi, Daniel Cremers, Ian Reid, Stefan Roth, Konrad Schindler, Laura Leal-Taixe: CVPR19 Tracking and Detection Challenge: How crowded can it get? 2019 mehr… BibTeX
  • Philipp Bergmann, Tim Meinhardt, Laura Leal-Taixe: Tracking without bells and whistles. 2019 mehr… BibTeX
  • Qadeer Khan, Patrick Wenzel, Daniel Cremers, Laura Leal-Taixé: Towards Generalizing Sensorimotor Control Across Weather Conditions. 2019 mehr… BibTeX
  • Qunjie Zhou, Torsten Sattler, Marc Pollefeys, Laura Leal-Taixe: To Learn or Not to Learn: Visual Localization from Essential Matrices. 2019 mehr… BibTeX
  • Torsten Sattler, Qunjie Zhou, Marc Pollefeys, Laura Leal-Taixe: Understanding the Limitations of CNN-based Absolute Camera Pose Regression. 2019 mehr… BibTeX

2018

  • Laude, Emanuel; Lange, Jan-Hendrik; Schupfer, Jonas; Domokos, Csaba; Leal-Taixe, Laura; Schmidt, Frank R.; Andres, Bjoern; Cremers, Daniel: Discrete-Continuous ADMM for Transductive Inference in Higher-Order MRFs. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, IEEE, 2018 mehr… BibTeX Volltext ( DOI )
  • Mengyu Chu, You Xie, Jonas Mayer, Laura Leal-Taixé, Nils Thuerey: Learning Temporal Coherence via Self-Supervision for GAN-based Video Generation. 2018 mehr… BibTeX
  • Ochs, Peter; Meinhardt, Tim; Leal-Taixe, Laura; Moeller, Michael: Lifting Layers: Analysis and Applications. In: Computer Vision – ECCV 2018. Springer International Publishing, 2018 mehr… BibTeX Volltext ( DOI )
  • S. Hamid Rezatofighi, Roman Kaskman, Farbod T. Motlagh, Qinfeng Shi, Daniel Cremers, Laura Leal-Taixé, Ian Reid: Deep Perm-Set Net: Learn to predict sets with unknown permutation and cardinality using deep neural networks. 2018 mehr… BibTeX

2017

  • Caelles, S.; Maninis, K. -K.; Pont-Tuset, J.; Leal-Taixe, L.; Cremers, D.; Gool, L. Van: One-Shot Video Object Segmentation. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, 2017 mehr… BibTeX Volltext ( DOI )
  • Walch, F.; Hazirbas, C.; Leal-Taixe, L.; Sattler, T.; Hilsenbeck, S.; Cremers, D.: Image-Based Localization Using LSTMs for Structured Feature Correlation. 2017 IEEE International Conference on Computer Vision (ICCV), IEEE, 2017 mehr… BibTeX Volltext ( DOI )