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Computer Science
Switzerland
2025

D-Index & Metrics

Computer Science

D-Index
66
Citations
16464
World Ranking
2337
National Ranking
55

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

  • Learning to Find Good Correspondences

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

  • Beyond Sharing Weights for Deep Domain Adaptation

    Artem Rozantsev;Mathieu Salzmann;Pascal Fua

  • Unsupervised Domain Adaptation by Domain Invariant Projection

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

  • Learning Trajectory Dependencies for Human Motion Prediction

    Wei Mao;Miaomiao Liu;Mathieu Salzmann;Hongdong Li

  • Discrete-Continuous Depth Estimation from a Single Image

    Miaomiao Liu;Mathieu Salzmann;Xuming He

  • Deep Subspace Clustering Networks

    Pan Ji;Tong Zhang;Hongdong Li;Mathieu Salzmann

  • Kernel Methods on the Riemannian Manifold of Symmetric Positive Definite Matrices

    Sadeep Jayasumana;Richard Hartley;Mathieu Salzmann;Hongdong Li

  • Learning the Number of Neurons in Deep Networks

    Jose M. Alvarez;Mathieu Salzmann

  • Segmentation-Driven 6D Object Pose Estimation

    Yinlin Hu;Joachim Hugonot;Pascal Fua;Mathieu Salzmann

  • History Repeats Itself: Human Motion Prediction via Motion Attention

    Wei Mao;Miaomiao Liu;Mathieu Salzmann

  • Kernel Methods on Riemannian Manifolds with Gaussian RBF Kernels

    Sadeep Jayasumana;Richard Hartley;Mathieu Salzmann;Hongdong Li

  • Structured Prediction of 3D Human Pose with Deep Neural Networks

    Bugra Tekin;Isinsu Katircioglu;Mathieu Salzmann;Vincent Lepetit

  • Evaluating The Search Phase of Neural Architecture Search

    Kaicheng Yu;Christian Sciuto;Martin Jaggi;Claudiu Musat

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

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

  • Unsupervised Geometry-Aware Representation for 3D Human Pose Estimation

    Helge Rhodin;Mathieu Salzmann;Pascal Fua

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

    Helge Rhodin;Frederic Meyer;Jorg Sporri;Erich Muller

  • 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

  • Learning cross-modality similarity for multinomial data

    Yangqing Jia;Mathieu Salzmann;Trevor Darrell

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

    Mehrtash Harandi;Mathieu Salzmann;Richard Hartley

  • Factorized Latent Spaces with Structured Sparsity

    Yangqing Jia;Mathieu Salzmann;Trevor Darrell

Frequent Co-Authors

Pascal Fua
Pascal Fua École Polytechnique Fédérale de Lausanne
Mehrtash Harandi
Mehrtash Harandi Monash University
Hongdong Li
Hongdong Li Australian National University
Richard Hartley
Richard Hartley Australian National University
Lars Petersson
Lars Petersson Commonwealth Scientific and Industrial Research Organisation
Xuming He
Xuming He Washington University in St. Louis
Stephen Gould
Stephen Gould Australian National University
Raquel Urtasun
Raquel Urtasun University of Toronto
Vincent Lepetit
Vincent Lepetit École des Ponts ParisTech
Nick Barnes
Nick Barnes Australian National University

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