| Multiple Instance Learning: A Survey of Problem Characteristics and Applications MA Carbonneau, V Cheplygina, E Granger, G Gagnon Pattern Recognition 77, 329-353, 2018 | 727 | 2018 |
| Boundary loss for highly unbalanced segmentation H Kervadec, J Bouchtiba, C Desrosiers, E Granger, J Dolz, I Ben Ayed Medical Image Analysis 67, 2021 | 555 | 2021 |
| Decoupling Direction and Norm for Efficient Gradient-Based L2 Adversarial Attacks and Defenses J Rony, LG Hafemann, LS Oliveira, I Ben Ayed, R Sabourin, E Granger CVPR 2019: IEEE Conference on Computer Vision and Pattern Recognition, Long …, 2019 | 359 | 2019 |
| Constrained-CNN losses for weakly supervised segmentation H Kervadec, J Dolz, M Tang, E Granger, Y Boykov, IB Ayed Medical Image Analysis 54, 88-99, 2019 | 291 | 2019 |
| Laplacian Regularized Few-Shot Learning IM Ziko, J Dolz, E Granger, I Ben Ayed ICML 2020: Int'l Conference on Machine Learning, Vienna, Austria, 2020 | 202 | 2020 |
| A unifying mutual information view of metric learning: cross-entropy vs. pairwise losses M Boudiaf, J Rony, IM Ziko, E Granger, M Pedersoli, P Piantanida, ... ECCV 2020: European Conf. on Computer Vision, Glasgow, UK., 548-564, 2020 | 188 | 2020 |
| A what-and-where fusion neural network for recognition and tracking of multiple radar emitters E Granger, M Rubin, S Grossberg, P Lavoie Neural Networks 14 (3), 325-344, 2001 | 161 | 2001 |
| Image Synthesis with Adversarial Networks: a Comprehensive Survey and Case Studies P Shamsolmoali, M Zareapoor, E Granger, H Zhou, R Wang, ME Celebi, ... Information Fusion 72, 126-146, 2021 | 152 | 2021 |
| The Tenth Visual Object Tracking VOT2022 Challenge Results Kristan, Matej, A Leonardis, J Matas, M Felsberg, R Pflugfelder, ... ECCVw 2022: European Conference on Computer Vision, Tel Aviv, Israel, 2022 | 147 | 2022 |
| A survey of techniques for incremental learning of HMM parameters W Khreich, E Granger, A Miri, R Sabourin Information Sciences 197, 105-130, 2012 | 147 | 2012 |
| Iterative Boolean combination of classifiers in the ROC space: An application to anomaly detection with HMMs W Khreich, E Granger, A Miri, R Sabourin Pattern Recognition 43 (8), 2732-2752, 2010 | 141 | 2010 |
| An adaptive classification system for video-based face recognition JF Connolly, E Granger, R Sabourin Information Sciences 192, 50-70, 2012 | 138 | 2012 |
| Unsupervised Domain Adaptation in the Dissimilarity Space for Person Re-identification D Mekhazni, A Bhuiyan, G Ekladious, E Granger ECCV 2020: European Conf. on Computer Vision, Glasgow, UK., 2020 | 137 | 2020 |
| Multi-feature extraction and selection in writer-independent off-line signature verification D Rivard, E Granger, R Sabourin International Journal on Document Analysis and Recognition (IJDAR) 16, 83-103, 2013 | 125 | 2013 |
| Dynamic selection of generative–discriminative ensembles for off-line signature verification L Batista, E Granger, R Sabourin Pattern Recognition 45 (4), 1326-1340, 2012 | 119 | 2012 |
| Hybrid writer‐independent–writer‐dependent offline signature verification system GS Eskander, R Sabourin, E Granger IET biometrics 2 (4), 169-181, 2013 | 106 | 2013 |
| Optics for high-performance servers and supercomputers AF Benner, DM Kuchta, PK Pepeljugoski, RA Budd, G Hougham, ... 2010 Conference on Optical Fiber Communication (OFC/NFOEC), collocated …, 2010 | 103 | 2010 |
| Deep learning in object detection and recognition X Jiang, A Hadid, Y Pang, E Granger, X Feng Springer 10, 978-981, 2019 | 101 | 2019 |
| Min-Max Entropy for Weakly Supervised Pointwise Localization S Belharbi, J Rony, J Dolz, I Ben Ayed, L McCaffrey, E Granger arXiv preprint arXiv:1907.12934, 2019 | 100* | 2019 |
| Bounding boxes for weakly supervised segmentation: Global constraints get close to full supervision H Kervadec, J Dolz, E Granger, I Ben Ayed MIDL 2020: Int'l Conf. on Medical Imaging with Deep Learning, Montreal, Canada., 2020 | 97 | 2020 |
Robert SabourinProfessor emeritus @ École de technologie supérieure - LIVIA - REPARTIVerified email at etsmtl.ca
