Multiple Instance Learning: A Survey of Problem Characteristics and Applications MA Carbonneau, V Cheplygina, E Granger, G Gagnon Pattern Recognition 77, 329-353, 2018 | 733 | 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 | 558 | 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 | 364 | 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 | 292 | 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 | 204 | 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 | 191 | 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 |
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 | 153 | 2022 |
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 | 153 | 2021 |
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 |
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 | 138 | 2020 |
An adaptive classification system for video-based face recognition JF Connolly, E Granger, R Sabourin Information Sciences 192, 50-70, 2012 | 138 | 2012 |
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 | 126 | 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 | 120 | 2012 |
Hybrid writer‐independent–writer‐dependent offline signature verification system GS Eskander, R Sabourin, E Granger IET biometrics 2 (4), 169-181, 2013 | 107 | 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 | 102 | 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 | 102* | 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 | 101 | 2020 |