| Context-aware crowd counting W Liu, M Salzmann, P Fua Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2019 | 774 | 2019 |
| Deep subspace clustering networks P Ji, T Zhang, H Li, M Salzmann, I Reid Advances in neural information processing systems 30, 2017 | 658 | 2017 |
| Learning to find good correspondences KM Yi, E Trulls, Y Ono, V Lepetit, M Salzmann, P Fua Proceedings of the IEEE conference on computer vision and pattern …, 2018 | 643 | 2018 |
| Beyond sharing weights for deep domain adaptation A Rozantsev, M Salzmann, P Fua IEEE transactions on pattern analysis and machine intelligence 41 (4), 801-814, 2018 | 563 | 2018 |
| Unsupervised Domain Adaptation by Domain Invariant Projection M Baktashmotlagh, MT Harandi, BC Lovell, M Salzmann International Conference on Computer Vision (ICCV), 2013 | 552 | 2013 |
| Learning trajectory dependencies for human motion prediction W Mao, M Liu, M Salzmann, H Li Proceedings of the IEEE/CVF international conference on computer vision …, 2019 | 518 | 2019 |
| Learning the number of neurons in deep networks JM Alvarez, M Salzmann Advances in neural information processing systems 29, 2016 | 504 | 2016 |
| Discrete-continuous depth estimation from a single image M Liu, M Salzmann, X He Proceedings of the IEEE conference on computer vision and pattern …, 2014 | 477 | 2014 |
| Evaluating the search phase of neural architecture search K Yu, C Sciuto, M Jaggi, C Musat, M Salzmann arXiv preprint arXiv:1902.08142, 2019 | 419 | 2019 |
| Segmentation-driven 6d object pose estimation Y Hu, J Hugonot, P Fua, M Salzmann Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2019 | 372 | 2019 |
| Kernel methods on the Riemannian manifold of symmetric positive definite matrices S Jayasumana, R Hartley, M Salzmann, H Li, M Harandi proceedings of the IEEE Conference on Computer Vision and Pattern …, 2013 | 364 | 2013 |
| Compression-aware training of deep networks JM Alvarez, M Salzmann Advances in neural information processing systems 30, 2017 | 340 | 2017 |
| Structured prediction of 3d human pose with deep neural networks B Tekin, I Katircioglu, M Salzmann, V Lepetit, P Fua arXiv preprint arXiv:1605.05180, 2016 | 340 | 2016 |
| History repeats itself: Human motion prediction via motion attention W Mao, M Liu, M Salzmann Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23 …, 2020 | 331 | 2020 |
| Learning to fuse 2d and 3d image cues for monocular body pose estimation B Tekin, P Márquez-Neila, M Salzmann, P Fua Proceedings of the IEEE international conference on computer vision, 3941-3950, 2017 | 314 | 2017 |
| Unsupervised geometry-aware representation for 3d human pose estimation H Rhodin, M Salzmann, P Fua Proceedings of the European conference on computer vision (ECCV), 750-767, 2018 | 306 | 2018 |
| Learning monocular 3d human pose estimation from multi-view images H Rhodin, J Spörri, I Katircioglu, V Constantin, F Meyer, E Müller, ... Proceedings of the IEEE conference on computer vision and pattern …, 2018 | 296 | 2018 |
| Kernel methods on Riemannian manifolds with Gaussian RBF kernels S Jayasumana, R Hartley, M Salzmann, H Li, M Harandi IEEE transactions on pattern analysis and machine intelligence 37 (12), 2464 …, 2015 | 284 | 2015 |
| Learning cross-modality similarity for multinomial data Y Jia, M Salzmann, T Darrell 2011 international conference on computer vision, 2407-2414, 2011 | 242 | 2011 |
| From manifold to manifold: Geometry-aware dimensionality reduction for SPD matrices MT Harandi, M Salzmann, R Hartley Computer Vision–ECCV 2014: 13th European Conference, Zurich, Switzerland …, 2014 | 240 | 2014 |
Pascal FuaProfessor Computer Science, EPFL在 epfl.ch 的电子邮件经过验证
