Mathieu Salzmann
EPFL IC IINFCOM CVLAB
BC 309 (Bâtiment BC)
Station 14
CH-1015 Lausanne
Web site: Web site: https://cvlab.epfl.ch/
EPFL IC IINFCOM CVLAB
BC 309 (Bâtiment BC)
Station 14
CH-1015 Lausanne
Web site: Web site: https://sin.epfl.ch
EPFL IC IINFCOM CVLAB
BC 309 (Bâtiment BC)
Station 14
CH-1015 Lausanne
Web site: Web site: https://ssc.epfl.ch
Fields of expertise
Computer Vision
Machine Learning
Machine Learning
News
- I am Area Chair for NeurIPS 2022- I am Area Chair for ECCV 2022
- 3 papers accepted to CVPR 2022 (2 orals)
- I am Area Chair for CVPR 2022
- 1 paper accepted to AAAI 2021
- I am Area Chair for CVPR 2021
- I am Associate Editor for IEEE TPAMI
- As of May 1st 2020, I have a 50% position with ClearSpace
Current work
My main focus is on developing algorithms at the intersection of machine learning and geometry in computer vision. I have also recently become highly interested in applications of machine learning and computer vision in space. My main interests are:- 6D pose estimation / 3D reconstruction
- Domain adaptation
- Compact and robust deep networks
- Neural architecture search
Biography
I am a Senior Researcher at EPFL-CVLab, and, since May 2020, an Artificial Intelligence Engineer at ClearSpace (50%). Previously, I was a Senior Researcher and Research Leader in NICTAPublications
Other publications
Publications
Conference Papers
- Z. Li, Y. Hu, M. Salzmann, and X. Ji. SD-Pose: Semantic Decomposition for Cross-Domain 6D Object Pose Estimation. AAAI Conference on Artificial Intelligence (AAAI), 2021.
- S. Guo, J. M. Alvarez, and M. Salzmann. ExpandNets: Linear Over-Parameterization to Train Compact Convolutional Networks. Neural Information Processing Systems (NeurIPS), 2020 (spotlight).
- C. Liu, M. Salzmann, T. Lin, R. Tomioka, and S. Süsstrunk. On the Loss Landscape of Adversarial Training: Identifying Challenges and How to Overcome Them. Neural Information Processing Systems (NeurIPS), 2020.
- K. K. Nakka and, M. Salzmann. Towards Robust Fine-grained Recognition by Maximal Separation of Discriminative Features. Asian Conference on Computer Vision (ACCV), 2020.
- T. Lebailly, S. Kiciroglu, M. Salzmann, P. Fua, and W. Wang. Motion Prediction Using Temporal Inception Module. Asian Conference on Computer Vision (ACCV), 2020.
- Z. Deng, J. Bednarik, M. Salzmann, and P. Fua. Better Patch Stitching for Parametric Surface Reconstruction. International Conference on 3D Vision (3DV), 2020.
- K. K. Nakka, and M. Salzmann. Indirect Local Attacks for Context-aware Semantic Segmentation Networks. European Conference on Computer Vision (ECCV), 2020 (spotlight).
- S. Kim, S. Süsstrunk, and M. Salzmann. Volumetric Transformer Networks. European Conference on Computer Vision (ECCV), 2020.
- W. Liu, M. Salzmann, and P. Fua. Estimating People Flows to Better Count Them in Crowded Scenes. European Conference on Computer Vision (ECCV), 2020.
- W. Mao, M. Liu, and M. Salzmann. History Repeats Itself: Human Motion Prediction via Motion Attention. European Conference on Computer Vision (ECCV), 2020.
- Y. Hu, W. Wang, P. Fua, and M. Salzmann. Single-Stage 6D Object Pose Estimation. Conference on Computer Vision and Pattern Recognition (CVPR), 2020.
- D. Bhattacharjee, S. Kim, G. Vizier, and M. Salzmann. DUNIT: Detection-based Unsupervised Image-to-image Translation. Conference on Computer Vision and Pattern Recognition (CVPR), 2020.
- S. Kiciroglu, H. Rhodin, S. Sinha, M. Salzmann, and P. Fua. ActiveMoCap: Optimized Viewpoint Selection for Active Human Motion Capture. Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (oral).
- J. Bednarik, S. Parashar, E. Gundodgu, M. Salzmann, and P. Fua. Shape Reconstruction by Learning Differentiable Surface Representations. Conference on Computer Vision and Pattern Recognition (CVPR), 2020.
- S. Parashar, M. Salzmann, and P. Fua. Local Non-Rigid Structure-from-Motion from Diffeomorphic Mappings. Conference on Computer Vision and Pattern Recognition (CVPR), 2020.
- M. S. Ali Akbarian, F. Saleh, M. Salzmann, L. Petersson, and S. Gould. A Stochastic Conditioning Scheme for Diverse Human Motion Prediction. Conference on Computer Vision and Pattern Recognition (CVPR), 2020.
- K. Yu, C. Sciuto, M. Jaggi, C. Musat, and M. Salzmann. Evaluating the Search Phase of Neural Architecture Search. International Conference on Learning Representations (ICLR), 2020.
- R. Bermudez-Chacon, M. Salzmann, and P. Fua. Domain Adaptive Multibranch Networks. International Conference on Learning Representations (ICLR), 2020.
- W. Wang, Z. Dang, Y. Hu, P. Fua, and M. Salzmann. Backpropagation-Friendly Eigendecomposition. Neural Information Processing Systems (NeurIPS), 2019.
- W. Mao, M. Liu, M. Salzmann, and H. Li. Learning Trajectory Dependencies for Human Motion Prediction. International Conference on Computer Vision (ICCV), 2019 (oral).
- K. Lis, K. K. Nakka, P. Fua, and M. Salzmann. Detecting the Unexpected via Image Resynthesis. International Conference on Computer Vision (ICCV), 2019.
- W. Wang, K. Yu, J. Hugonot, P. Fua, and M. Salzmann. Recurrent U-Net for Resource-Constrained Segmentation. International Conference on Computer Vision (ICCV), 2019.
- E. Gundogdu, V. Constantin, A. Seifoddini, M. Dang, M. Salzmann, and P. Fua. GarNet: A Two-Stream Network for Fast and Accurate 3D Cloth Draping. International Conference on Computer Vision (ICCV), 2019.
- Y. Benyahia, K. Yu, K. Bennani Smires, M. Jaggi, A. C. Davison, M. Salzmann, and C. Musat. Overcoming Multi-model Forgetting. International Conference on Machine Learning (ICML), 2019.
- Y. Hu, J. Hugonot, P. Fua and M. Salzmann. Segmentation-driven 6D Object Pose Estimation. Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, June 2019.
- W. Liu, M. Salzmann and P. Fua. Context-aware Crowd Counting. Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, June 2019.
- H. Rhodin, V. Constantin, I. Katircioglu, M. Salzmann and P. Fua. Neural Scene Decomposition for Multi-Person Motion Capture. Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, June 2019.
- M. Baktashmotlagh, M. Faraki, T. Drummond and M. Salzmann. Learning Factorized Representations for Open-set Domain Adaptation. International Conference on Learning Representations (ICLR), New Orleans, LA, May 2019.
- M. S. Aliakbarian, F. Saleh, M. Salzmann, B. Fernando, L. Petersson and L. Andersson. VIENA2: A Driving Anticipation Dataset. Asian Conference on Computer Vision (ACCV), Perth, Australia, December 2018.
- K. Yu and M. Salzmann. Statistically-motivated Second-order Pooling. European Conference on Computer Vision (ECCV), Munich, Germany, September 2018.
- Z. Dang, K. M. Yi, Y. Hu, F. Wang, P. Fua and M. Salzmann. Eigendecomposition-free Training of Deep Networks with Zero Eigenvalue-based Losses. European Conference on Computer Vision (ECCV), Munich, Germany, September 2018.
- F. Saleh, M. S. Aliakbarian, M. Salzmann, L. Petersson and J. M. Alvarez. Effective Use of Synthetic Data for Urban Scene Semantic Segmentation. European Conference on Computer Vision (ECCV), Munich, Germany, September 2018.
- H. Rhodin, M. Salzmann and P. Fua. Unsupervised Geometry-Aware Representation for 3D Human Pose Estimation. European Conference on Computer Vision (ECCV), Munich, Germany, September 2018 (oral).
- J. Bednarik, P. Fua and M. Salzmann. Learning to Reconstruct Texture-less Deformable Surfaces. International Conference on 3D Vision (3DV), Verona, Italy, September 2018.
- K. Nakka and M. Salzmann. Deep Attentional Structured Representation Learning for Visual Recognition. British Machine Vision Conference (BMVC), Newcastle, UK, September 2018.
- M. Kozinski, A. Mosinska, M. Salzmann and P. Fua. Learning to Segment 3D Linear Structures Using Only 2D Annotations. International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Granada, Spain, September 2018.
- K. M. Yi, E. Trulls, Y. Ono, V. Lepetit, M. Salzmann and P. Fua. Learning to Find Good Correspondences. Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, June 2018 (oral).
- H. Rhodin, J. Spoerri, I. Katircioglu, V. Constantin, F. Meyer, E. Mueller, M. Salzmann and P. Fua. Learning Monocular 3D Human Pose Estimation From Multi-View Images. Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, June 2018 (spotlight).
- M. Liu, X. He and M. Salzmann. Geometry-Aware Deep Network for Single-Image Novel View Synthesis. Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, June 2018.
- A. Rozantsev, M. Salzmann and P. Fua. Residual Parameter Transfer for Deep Domain Adaptation. Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, June 2018.
- R. Bermudez Chacon, P. Marquez Neila, M. Salzmann and P. Fua. A Domain-Adaptive Two-Stream U-Net for Electron Microscopy. Image Segmentation International Symposium on Biomedical Imaging (ISBI), Washington, D.C., April 2018.
- W. Zhuo, M. Salzmann, X. He and M. Liu. 3D Box Proposal from a Monocular Image for Indoor Scenes. AAAI Conference on Artificial Intelligence (AAAI), New Orleans, LA, February 2018.
- J. M. Alvarez and M. Salzmann. Compression-aware Training of Deep Networks. Neural Information Processing Systems (NIPS), Long Beach, CA, December 2017.
- P. Ji, T. Zhang, H. Li, M. Salzmann and I. Reid. Deep Subspace Clustering Networks. Neural Information Processing Systems (NIPS), Long Beach, CA, December 2017.
- T. Probst, A. Fossati, M. Salzmann and L. Van Gool. Efficient Model-free Anthropometry from Depth Data. International Conference on 3D Vision (3DV), Qingdao, China, October 2017.
- B. Tekin, P. Marquez Neila, M. Salzmann and P. Fua. Learning to Fuse 2D and 3D Image Cues for Monocular Body Pose Estimation. International Conference on Computer Vision (ICCV), Venice, Italy, October 2017.
- M. S. Aliakbarian, F. Saleh, M. Salzmann, B. Fernando, L. Petersson and L. Andersson. Encouraging LSTMs to Anticipate Actions Very Early. International Conference on Computer Vision (ICCV), Venice, Italy, October 2017.
- F. Saleh, M. S. Aliakbarian, M. Salzmann, L. Petersson and J. M. Alvarez. Bringing Background into the Foreground: Making All Classes Equal in Weakly-supervised Video Semantic Segmentation. International Conference on Computer Vision (ICCV), Venice, Italy, October 2017.
- M. Harandi, M. Salzmann and R. Hartley. Joint Dimensionality Reduction and Metric Learning: A Geometric Take. International Conference on Machine Learning (ICML), Sydney, Australia, August 2017.
- Z. Hayder, X. He and M. Salzmann. Boundary-aware Instance Segmentation. Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, July 2017.
- A. Thalaiyasingam, A. Desmaison, R. Bunel, M. Salzmann, P. H. S. Torr and M. P. Kumar. Efficient Linear Programming for Dense CRFs. Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, July 2017.
- W. Zhuo, M. Salzmann, X. He and M. Liu. Indoor Scene Parsing with Instance Segmentation, Semantic Labeling and Support Relationship Inference. Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, July 2017.
- J. M. Alvarez and M. Salzmann. Learning the Number of Neurons in Deep Networks. Neural Information Processing Systems (NIPS), Barcelona, Spain, December 2016.
- M. Liu, X. He and M. Salzmann. Building Scene Models by Completing and Hallucinating Depth and Semantics. European Conference on Computer Vision (ECCV), Amsterdam, Netherlands, October 2016.
- F. Saleh, M. S. Ali Akbarian, M. Salzmann, L. Petersson, S. Gould and J. M. Alvarez. Built-in Foreground/Background Prior for Weakly-Supervised Semantic Segmentation. European Conference on Computer Vision (ECCV), Amsterdam, Netherlands, October 2016.
- X. Wang, M. Salzmann, F. Wang and J. Zhao. Template-free 3D Reconstruction of Poorly-textured Nonrigid Surfaces. European Conference on Computer Vision (ECCV), Amsterdam, Netherlands, October 2016.
- R. Bermudez Chacon, C. Becker, M. Salzmann and P. Fua. Scalable Unsupervised Domain Adaptation for Electron Microscopy. International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Athens, Greece, October 2016.
- B. Tekin, I. Katircioglu, M. Salzmann, V. Lepetit and P. Fua. Structured Prediction of 3D Human Pose with Deep Neural Networks. British Machine Vision Conference (BMVC), York, UK, September 2016 (oral).
- P. Ji, H. Li, M. Salzmann and Y. Zhong. Robust Multi-body Feature Tracker: A Segmentation-free Approach. Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, AZ, June 2016 (spotlight).
- Z. Hayder, X. He and M. Salzmann. Learning to Co-Generate Object Proposals with a Deep Structured Network. Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, AZ, June 2016.
- M. Harandi, M. Salzmann and F. Porikli. When VLAD met Hilbert. Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, AZ, June 2016.
- M. Najafi, S. Taghavi Namin, M. Salzmann and L. Petersson. Sample and Filter: Nonparametric Scene Parsing via Efficient Filtering. Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, AZ, June 2016.
- A. Thalaiyasingam, R. Hartley and M. Salzmann. Memory Efficient Max Flow for Multi-label Submodular MRFs. Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, AZ, June 2016.
- J. M. Alvarez, M. Salzmann and N. Barnes. Efficient Transductive Semantic Segmentation. Winter Conference on Applications of Computer Vision (WACV), Lake Placid, NY, March 2016.
- A. Khan, S. Gould and M. Salzmann. Segmentation of Developing Human Embryo in Time-Lapse Microscopy. International Symposium on Biomedical Imaging (ISBI), Prague, Czech Republic, April 2016.
- P. Ji, H. Li and M. Salzmann. Shape Interaction Matrix Revisited and Robustified: Efficient Subspace Clustering with Corrupted and Incomplete Data. International Conference on Computer Vision (ICCV), Santiago de Chile, December 2015 (oral).
- M. Harandi, M. Salzmann and M. Baktashmotlagh. Beyond Gauss: Image-Set Matching on the Riemannian Manifold of PDFs. International Conference on Computer Vision (ICCV), Santiago de Chile, December 2015.
- S. Taghavi Namin, M. Najafi, M. Salzmann and L. Petersson. Cutting Edge: Soft Correspondences in Multimodal Scene Parsing. International Conference on Computer Vision (ICCV), Santiago de Chile, December 2015.
- W. Xu, M. Salzmann, Y. Wang and Y. Liu. Deformable 3D Fusion: From Partial Dynamic 3D Observations to Complete 4D Models. International Conference on Computer Vision (ICCV), Santiago de Chile, December 2015.
- Z. Hayder, X. He and M. Salzmann. Structural Kernel Learning for Large Scale Multiclass Object Co-Detection. International Conference on Computer Vision (ICCV), Santiago de Chile, December 2015.
- L. Horne, J. M. Alvarez, M. Salzmann and N. Barnes. Efficient Scene Parsing by Sampling Unary Potentials in a Fully-Connected CRF. Intelligent Vehicles Symposium (IV), Seoul, Korea, June 2015.
- M. Harandi and M. Salzmann. Riemannian Coding and Dictionary Learning: Kernels to the Rescue. Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, June 2015. Supplementary material.
- W. Zhuo, M. Salzmann, X. He and M. Liu. Indoor Scene Structure Analysis for Single Image Depth Estimation. Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, June 2015.
- A. Thalaiyasingam, R. Hartley, M. Salzmann and H. Li. Iteratively Reweighted Graph Cut for Multi-label MRFs with Non-convex Priors. Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, June 2015.
- A. Khan, S. Gould and M. Salzmann. Automated Monitoring of Human Embryonic Cells up to the 5-Cell Stage in Time-Lapse Microscopy Images. International Symposium on Biomedical Imaging (ISBI), New York, NY, April 2015.
- A. Khan, S. Gould and M. Salzmann. A Linear Chain Markov Model for Detection and Localization of Cells in Early Stage Embryo Development. Winter Conference on Applications of Computer Vision (WACV), Waikoloa Beach, HI, January 2015.
- S. Taghavi Namin, M. Najafi, M. Salzmann and L. Petersson. A Multi-modal Graphical Model for Scene Analysis. Winter Conference on Applications of Computer Vision (WACV), Waikoloa Beach, HI, January 2015.
- M. Harandi, M. Salzmann and R. Hartley. From Manifold to Manifold: Geometry-Aware Dimensionality Reduction for SPD Matrices. European Conference on Computer Vision (ECCV), Zurich, Switzerland, September 2014 (oral). Supplementary material. Code.
- M. Harandi, M. Salzmann, S. Jayasumana, R. Hartley and H. Li. Expanding the Family of Grassmannian Kernels: An Embedding Perspective. European Conference on Computer Vision (ECCV), Zurich, Switzerland, September 2014. Supplementary material.
- Z. Hayder, M. Salzmann and X. He. Object Co-Detection via Efficient Inference in a Fully-Connected CRF. European Conference on Computer Vision (ECCV), Zurich, Switzerland, September 2014.
- P. Ji, H. Li, M. Salzmann and Y. Dai. Robust Motion Segmentation with Unknown Correspondences. European Conference on Computer Vision (ECCV), Zurich, Switzerland, September 2014.
- M. Najafi, S. Taghavi Namin, M. Salzmann and L. Petersson. Non-Associative Higher-Order Markov Networks for Point Cloud Classification. European Conference on Computer Vision (ECCV), Zurich, Switzerland, September 2014.
- W. Xu, M. Salzmann, Y. Wang and Y. Liu. Nonrigid Surface Registration and Completion from RGBD Images. European Conference on Computer Vision (ECCV), Zurich, Switzerland, September 2014. Supplementary material.
- P. Ji, Y. Zhong, H. Li and M. Salzmann. Null Space Clustering with Applications to Motion Segmentation and Face Clustering. International Conference on Image Processing (ICIP), Paris, France, October 2014 (best student paper award).
- S. Jayasumana, R. Hartley, M. Salzmann, H. Li and M. Harandi. Optimizing Over Radial Kernels on Compact Manifolds. Conference on Computer Vision and Pattern Recognition (CVPR), Columbus, OH, June 2014 (oral).
- M. Liu, M. Salzmann and X. He. Discrete-Continuous Depth Estimation from a Single Image. Conference on Computer Vision and Pattern Recognition (CVPR), Columbus, OH, June 2014.
- M. Harandi, M. Salzmann and F. Porikli. Bregman Divergences for Infinite Dimensional Covariance Matrices. Conference on Computer Vision and Pattern Recognition (CVPR), Columbus, OH, June 2014. Supplementary material.
- M. Baktashmotlagh, M. Harandi, B. Lovell, and M. Salzmann. Domain Adaptation on the Statistical Manifold. Conference on Computer Vision and Pattern Recognition (CVPR), Columbus, OH, June 2014.
- J. M. Alvarez, M. Salzmann and N. Barnes. Large-Scale Semantic Co-Labeling of Image Sets. Winter Conference on Applications of Computer Vision (WACV), Steamboat Springs, CO, March 2014.
- J. M. Alvarez, M. Salzmann and N. Barnes. Data-Driven Road Detection. Winter Conference on Applications of Computer Vision (WACV), Steamboat Springs, CO, March 2014.
- P. Ji, M. Salzmann and H. Li Efficient Dense Subspace Clustering Winter Conference on Applications of Computer Vision (WACV), Steamboat Springs, CO, March 2014. Code.
- M. Baktashmotlagh, M. Harandi, B. Lovell and M. Salzmann. Unsupervised Domain Adaptation by Domain Invariant Projection. International Conference on Computer Vision (ICCV), Sydney, Australia, December 2013.
- S. Jayasumana, M. Salzmann, H. Li and M. Harandi. A Framework for Shape Analysis via Hilbert Space Embedding. International Conference on Computer Vision (ICCV), Sydney, Australia, December 2013.
- S. Jayasumana, R. Hartley, M. Salzmann, H. Li and M. Harandi. Combining Multiple Manifold-valued Descriptors for Improved Object Recognition. International Conference on Digital Image Computing: Techniques and Applications (DICTA), Hobart, Australia, November 2013 (CiSRA best recognition paper award).
- M. Baktashmotlagh, M. Harandi, A. Bigdeli, B. Lovell and M. Salzmann. Non-Linear Stationary Subspace Analysis with Application to Video Classification. International Conference on Machine Learning (ICML), Atlanta, GA, June 2013. Supplementary material.
- M. Salzmann. Continuous Inference in Graphical Models with Polynomial Energies. Conference on Computer Vision and Pattern Recognition (CVPR), Portland, OR, June 2013. Code.
- S. Jayasumana, R. Hartley, M. Salzmann, H. Li and M. Harandi. Kernel Methods on the Riemannian Manifold of Symmetric Positive Definite Matrices. Conference on Computer Vision and Pattern Recognition (CVPR), Portland, OR, June 2013 (oral).
- M. Liu, R. Hartley and M. Salzmann. Mirror Surface Reconstruction from a Single Image. Conference on Computer Vision and Pattern Recognition (CVPR), Portland, OR, June 2013. Supplementary material.
- J. M. Alvarez, M. Salzmann and N. Barnes. Learning Appearance Models for Road Detection. Intelligent Vehicles Symposium (IV), Brisbane, Australia, June 2013.
- M. Salzmann and R. Urtasun. Beyond Feature Points: Structured Prediction for Monocular Non-rigid 3D Reconstruction. European Conference on Computer Vision (ECCV), Firenze, Italy, October 2012.
- A. Varol, M. Salzmann, P. Fua and R. Urtasun. A Constrained Latent Variable Model. Conference on Computer Vision and Pattern Recognition (CVPR), Providence, RI, June 2012. Proofs.
- M. Brubaker, M. Salzmann and R. Urtasun. A Family of MCMC Methods on Implicitly Defined Manifolds. International Conference on Artificial Intelligence and Statistics (AISTATS), La Palma, Canary Islands, April 2012. Code.
- M. Salzmann and R. Urtasun. Physically-based Motion Models for 3D Tracking: A Convex Formulation. International Conference on Computer Vision (ICCV), Barcelona, November 2011.
- Y. Jia, M. Salzmann and T. Darrell. Learning Cross-modality Similarity for Multinomial Data. International Conference on Computer Vision (ICCV), Barcelona, November 2011. Dataset.
- M. Salzmann and R. Urtasun. Implicitly Constrained Gaussian Process Regression for Monocular Non-Rigid Pose Estimation. Neural Information Processing Systems Conference (NIPS), Vancouver, Canada, December 2010.
- Y. Jia, M. Salzmann and T. Darrell. Factorized Latent Spaces with Structured Sparsity. Neural Information Processing Systems Conference (NIPS), Vancouver, Canada, December 2010.
- C. M. Christoudias, R. Urtasun, M. Salzmann and T. Darrell. Learning to Recognize Objects from Unseen Modalities. European Conference on Computer Vision (ECCV), Heraklion, Crete, September 2010. Code dataset.
- M. Salzmann and R. Urtasun. Combining Discriminative and Generative Methods for 3D Deformable Surface and Articulated Pose Reconstruction. Conference on Computer Vision and Pattern Recognition (CVPR), San Francisco, CA, June 2010 (oral).
- M. Salzmann, C. H. Ek, R. Urtasun and T. Darrell. Factorized Orthogonal Latent Spaces. International Conference on Artificial Intelligence and Statistics (AISTATS), Sardinia, Italy, May 2010.
- A. Varol, M. Salzmann, E. Tola and P. Fua. Template-Free Monocular Reconstruction of Deformable Surfaces. International Conference on Computer Vision (ICCV), Kyoto, Japan, October 2009.
- M. Salzmann and P. Fua. Reconstructing Sharply Folding Surfaces: A Convex Formulation. Conference on Computer Vision and Pattern Recognition (CVPR), Miami, FL, June 2009 (oral). Code.
- F. Moreno-Noguer, M. Salzmann, V. Lepetit and P. Fua. Capturing 3D Stretchable Surfaces from Single Images in Closed Form. Conference on Computer Vision and Pattern Recognition (CVPR), Miami, FL, June 2009.
- A. Fossati, M. Salzmann and P. Fua. Observable Subspaces for 3D Human Motion Recovery. Conference on Computer Vision and Pattern Recognition (CVPR), Miami, FL, June 2009.
- M. Salzmann, F. Moreno-Noguer, V. Lepetit and P. Fua. Closed-Form Solution to Non-Rigid 3D Surface Registration. European Conference on Computer Vision (ECCV), Marseille, France, October 2008.
- M. Salzmann, R. Urtasun and P. Fua. Local Deformation Models for Monocular 3D Shape Recovery. Conference on Computer Vision and Pattern Recognition (CVPR), Anchorage, Alaska, June 2008 (oral).
- P. Lagger, M. Salzmann, V. Lepetit and P. Fua. 3D Pose Refinement from Reflections. Conference on Computer Vision and Pattern Recognition (CVPR), Anchorage, Alaska, June 2008 (oral).
- M. Salzmann, R. Hartley and P. Fua. Convex Optimization for Deformable Surface 3-D Tracking. IEEE International Conference on Computer Vision (ICCV), Rio de Janeiro, Brazil, October 2007 (oral).
- M. Salzmann, V. Lepetit and P. Fua. Deformable Surface Tracking Ambiguities. Conference on Computer Vision and Pattern Recognition (CVPR), Minneapolis, MI, June 2007.
- M. Salzmann, S. Ilic and P. Fua. Physically Valid Shape Parameterization for Monocular 3-D Deformable Surface Tracking. British Machine Vision Conference (BMVC), Oxford Brookes, September 2005.
- S. Ilic, M. Salzmann and P. Fua. Implicit Surfaces Make for Better Silhouettes. Conference on Computer Vision and Pattern Recognition (CVPR), San Diego, CA, June 2005.
Books and Book Chapters
- M. Baktashmotlagh, M. Harandi and M. Salzmann. Learning Domain Invariant Embeddings by Matching Distributions. In Domain Adaptation in Computer Vision Applications, Springer 2017.
- M. Harandi, R. Hartley, M. Salzmann and J. Trumpf. Dictionary Learning on Grassmann Manifolds. In Algorithmic Advances in Riemannian Geometry and Applications, Springer, 2016.
- S. Jayasumana, R. Hartley and M. Salzmann. Kernels on Riemannian Manifolds. In Riemannian Computing in Computer Vision, Springer, 2016.
- M. Salzmann and P. Fua. Deformable Surface 3D Reconstruction from Monocular Images. Morgan & Claypool, Synthesis Lectures on Computer Vision, 2010.
Journal Papers
- Z. Dang, K. M. Yi, Y. Hu, F. Wang, P. Fua, and M. Salzmann. Eigendecomposition-Free Training of Deep Networks for Linear Least-Square Problems. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020.
- A. Rozantsev, M. Salzmann and P. Fua. Beyond Sharing Weights for Deep Domain Adaptation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019.
- F. Saleh, M. S. Ali Akbarian, M. Salzmann, L. Petersson, J. M. Alvarez and S. Gould. Incorporating Network Built-in Priors in Weakly-supervised Semantic Segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018.
- M. Harandi, M. Salzmann and R. Hartley. Dimensionality Reduction on SPD Manifolds: The Emergence of Geometry-Aware Methods. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017.
- J. M. Alvarez, M. Salzmann and N. Barnes. Exploiting Large Image Sets for Road Scene Parsing. IEEE Transactions on Intelligent Transportation Systems, Vol. 17, Nr. 9, pp. 2456-2465, September 2016.
- M. Baktashmotlagh, M. Harandi and M. Salzmann. Distribution-Matching Embedding for Visual Domain Adaptation. Journal of Machine Learning Research (JMLR), Vol. 17, pp. 1-30, August 2016.
- L. Horne, J. M. Alvarez, C. McCarthy, M. Salzmann and N. Barnes. Semantic Labeling for Prosthetic Vision. Computer Vision and Image Understanding, Vol. 149, pp. 113-125, August 2016.
- S. Jayasumana, R. Hartley, M. Salzmann, H. Li and M. Harandi. Kernel Methods on Riemannian Manifolds with Gaussian RBF Kernels. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 37 , Nr. 12, pp. 2464-2477, December 2015.
- M. Liu, R. Hartley and M. Salzmann. Mirror Surface Reconstruction from a Single Image. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 37, Nr. 4, pp. 760-773, April 2015.
- M. Baktashmotlagh, M. Harandi, B. Lovell and M. Salzmann. Discriminative Non-Linear Stationary Subspace Analysis for Video Classification. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 36, Nr. 12, pp. 2353-2366, December 2014.
- A. Varol, A. Shaji, M. Salzmann and P. Fua. Monocular 3D Reconstruction of Locally Textured Surfaces. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 34, Nr. 6, pp. 1118-1130, June 2012.
- M. Salzmann and P. Fua. Linear Local Models for Monocular Reconstruction of Deformable Surfaces. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 33, Nr. 5, pp. 931-944, May 2011.
- M. Salzmann, J. Pilet, S. Ilic and P. Fua. Surface Deformation Models for Non-Rigid 3-D Shape Recovery. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 29, Nr. 8, pp. 1481 - 1487, August 2007.
- S. Ilic, M. Salzmann and P. Fua. Implicit Meshes for Effective Silhouette Handling. International Journal of Computer Vision, Vol. 72, Nr. 2, pp. 159 - 178, 2007.
Workshops
- K. K. Nakka and M. Salzmann. Interpretable BoW Networks for Adversarial Example Detection. Explainable AI Workshop, in conjunction with ICCV, 2019.
- P. Marquez Neila, M. Salzmann, and P. Fua. Imposing Hard Constraints on Deep Networks: Promises and Limitations. Workshop on Negative Results in Computer Vision, in conjunction with CVPR, 2017.
- A. Khan, S. Gould and M. Salzmann. Deep Convolutional Neural Networks for Human Embryonic Cell Counting. BioImage Computing, in conjunction with ECCV, October 2016.
- A. Khan, S. Gould and M. Salzmann. Detecting Abnormal Cell Division Patterns in Early Stage Human Embryo Development. Machine Learning in Medical Imaging, in conjunction with MICCAI, October 2015.
- A. Varol, M. Salzmann, P. Fua and R. Urtasun. Constrained Regression for 3D Pose Estimation. Learning Workshop (Snowbird), April 2011.
- M. Salzmann, C. H. Ek, R. Urtasun and T. Darrell. FOLS: Factorized Orthogonal Latent Spaces. Learning Workshop (Snowbird), April 2010 (oral).
- M. Salzmann and R. Urtasun. A Constrained Combination of Discriminative and Generative Methods. Learning Workshop (Snowbird), April 2010.
Technical Reports
- M. Salzmann, J. Pilet, S. Ilic, P. Fua. Parameterizing Deformable Surfaces for Monocular 3-D Tracking. EPFL Technical Report IC/2005/020, 2005.
- R. Urtasun, M. Salzmann, P. Fua. 3D Morphing without User Interaction. Technical Report, 2004.
Ph.D Thesis
- M. Salzmann Learning and Recovering 3D Surface Deformations EPFL Thesis N° 4270, January 2009.
Teaching & PhD
Teaching
Computer Science
Digital Humanities
Communication Systems
PhD Students
Bhattacharjee Deblina, Davydov Andrey, Javed Saqib, Li Shuangqi, Lis Krzysztof Maciej, Ozaydin Ali Baran, Vidit Vidit, Zhang Tianzong, Zhao Chen,Past EPFL PhD Students
Bednarík Jan , Guo Shuxuan , Katircioglu Isinsu , Kiciroglu Sena , Liu Chen , Nakka Krishna Kanth , Yu Kaicheng ,Courses
Machine learning for DH
This course aims to introduce the basic principles of machine learning in the context of the digital humanities. We will cover both supervised and unsupervised learning techniques, and study and implement methods to analyze diverse data types, such as images, music and social network data.