2026 Mathieu Salzmann: Computer Science Researcher – H-Index, Publications & Awards

Discipline name	D-Index	World Ranking	Current World Ranking	National Ranking	Current National Ranking	Publications	Citations
Computer Science	66	2337	2267	55	55	280	16464

Mathieu Salzmann publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Mathieu Salzmann sits on this spectrum.

32 publications 991+

This scientist: 280 publications — 69th percentile

69% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 991 publications or more.

Mathieu Salzmann D-index placement in Computer Science in 2026

The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Mathieu Salzmann sits on this spectrum.

30 D-Index 131+

This scientist: 66 D-Index — 84th percentile

84% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

Research.com Recognitions

2025 - Research.com Computer Science in Switzerland Leader Award
2022 - Research.com Computer Science in Switzerland Leader Award

Overview

Mathieu Salzmann is affiliated with the École Polytechnique Fédérale de Lausanne in Switzerland. Their research work spans several interconnected areas within computer science and engineering, with a strong focus on computer vision and pattern recognition.

Their recent papers include the following:

Learning trajectory dependencies for human motion prediction, 2024, ANU Open Research (Australian National University)
Multi-level Motion Attention for Human Motion Prediction, 2021, International Journal of Computer Vision
Templates for 3D Object Pose Estimation Revisited: Generalization to New Objects and Robustness to Occlusions, 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Progressive Correspondence Pruning by Consensus Learning, 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
SegmentMeIfYouCan: A Benchmark for Anomaly Segmentation, 2021, arXiv (Cornell University)

Frequent coauthors who have collaborated with Mathieu Salzmann include:

Pascal Fua
Sabine Süsstrunk
Yinlin Hu
Sina Honari
Zheng Dang

The most common venues for their publications are:

arXiv (Cornell University)
IEEE Transactions on Pattern Analysis and Machine Intelligence
Zenodo (CERN European Organization for Nuclear Research)
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
2021 IEEE/CVF International Conference on Computer Vision (ICCV)

Mathieu Salzmann has contributed to book publications, notably with Morgan & Claypool Publishers, including the title "Visual Domain Adaptation in the Deep Learning Era" published in 2022.

Their main fields of study are:

Computer Science
Engineering

Within these disciplines, their subfields of focus are:

Computer Vision and Pattern Recognition
Artificial Intelligence
Control and Systems Engineering
Computational Mechanics
Aerospace Engineering

Key topics covered in their research include:

Human Pose and Action Recognition
Advanced Neural Network Applications
Domain Adaptation and Few-Shot Learning
3D Shape Modeling and Analysis
Robotics and Sensor-Based Localization
Anomaly Detection Techniques and Applications
Video Surveillance and Tracking Methods

Best Publications

Context-Aware Crowd Counting

Weizhe Liu;Mathieu Salzmann;Pascal Fua

697
Citations
Learning to Find Good Correspondences

Kwang Moo Yi;Eduard Trulls;Yuki Ono;Vincent Lepetit

535
Citations
Beyond Sharing Weights for Deep Domain Adaptation

Artem Rozantsev;Mathieu Salzmann;Pascal Fua

517
Citations
Unsupervised Domain Adaptation by Domain Invariant Projection

Mahsa Baktashmotlagh;Mahsa Baktashmotlagh;Mehrtash T. Harandi;Mehrtash T. Harandi;Brian C. Lovell;Mathieu Salzmann;Mathieu Salzmann

509
Citations
Learning Trajectory Dependencies for Human Motion Prediction

Wei Mao;Miaomiao Liu;Mathieu Salzmann;Hongdong Li

501
Citations
Discrete-Continuous Depth Estimation from a Single Image

Miaomiao Liu;Mathieu Salzmann;Xuming He

435
Citations
Deep Subspace Clustering Networks

Pan Ji;Tong Zhang;Hongdong Li;Mathieu Salzmann

406
Citations
Kernel Methods on the Riemannian Manifold of Symmetric Positive Definite Matrices

Sadeep Jayasumana;Richard Hartley;Mathieu Salzmann;Hongdong Li

368
Citations
Learning the Number of Neurons in Deep Networks

Jose M. Alvarez;Mathieu Salzmann

325
Citations
Segmentation-Driven 6D Object Pose Estimation

Yinlin Hu;Joachim Hugonot;Pascal Fua;Mathieu Salzmann

311
Citations
History Repeats Itself: Human Motion Prediction via Motion Attention

Wei Mao;Miaomiao Liu;Mathieu Salzmann

293
Citations
Kernel Methods on Riemannian Manifolds with Gaussian RBF Kernels

Sadeep Jayasumana;Richard Hartley;Mathieu Salzmann;Hongdong Li

282
Citations
Structured Prediction of 3D Human Pose with Deep Neural Networks

Bugra Tekin;Isinsu Katircioglu;Mathieu Salzmann;Vincent Lepetit

281
Citations
Evaluating The Search Phase of Neural Architecture Search

Kaicheng Yu;Christian Sciuto;Martin Jaggi;Claudiu Musat

280
Citations
Learning to Fuse 2D and 3D Image Cues for Monocular Body Pose Estimation

Bugra Tekin;Pablo Marquez-Neila;Mathieu Salzmann;Pascal Fua

271
Citations
Unsupervised Geometry-Aware Representation for 3D Human Pose Estimation

Helge Rhodin;Mathieu Salzmann;Pascal Fua

259
Citations
Learning Monocular 3D Human Pose Estimation from Multi-view Images

Helge Rhodin;Frederic Meyer;Jorg Sporri;Erich Muller

247
Citations
From Manifold to Manifold: Geometry-Aware Dimensionality Reduction for SPD Matrices

Mehrtash Tafazzoli Harandi;Mehrtash Tafazzoli Harandi;Mathieu Salzmann;Mathieu Salzmann;Richard I. Hartley;Richard I. Hartley

242
Citations
Learning cross-modality similarity for multinomial data

Yangqing Jia;Mathieu Salzmann;Trevor Darrell

225
Citations
Dimensionality Reduction on SPD Manifolds: The Emergence of Geometry-Aware Methods

Mehrtash Harandi;Mathieu Salzmann;Richard Hartley

213
Citations
Factorized Latent Spaces with Structured Sparsity

Yangqing Jia;Mathieu Salzmann;Trevor Darrell

205
Citations

Frequent Co-Authors

Pascal Fua École Polytechnique Fédérale de Lausanne

Mehrtash Harandi Monash University

Hongdong Li Australian National University

Richard Hartley Australian National University

Lars Petersson Commonwealth Scientific and Industrial Research Organisation

Jose M. Alvarez Nvidia (United States)

Xuming He Washington University in St. Louis

Stephen Gould Australian National University

Raquel Urtasun University of Toronto

Vincent Lepetit École des Ponts ParisTech

External Links

Personal website of Mathieu Salzmann Google Scholar page

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