| Improving semantic embedding consistency by metric learning for zero-shot classiffication M Bucher, S Herbin, F Jurie Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The …, 2016 | 267 | 2016 |
| Semantic hierarchies for image annotation: A survey AM Tousch, S Herbin, JY Audibert Pattern Recognition 45 (1), 333-345, 2012 | 216 | 2012 |
| Generating visual representations for zero-shot classification M Bucher, S Herbin, F Jurie Proceedings of the IEEE International Conference on Computer Vision …, 2017 | 190 | 2017 |
| Processing of extremely high-resolution Lidar and RGB data: outcome of the 2015 IEEE GRSS data fusion contest–part a: 2-D contest M Campos-Taberner, A Romero-Soriano, C Gatta, G Camps-Valls, ... IEEE Journal of Selected Topics in Applied Earth Observations and Remote …, 2016 | 124 | 2016 |
| Benchmarking classification of earth-observation data: From learning explicit features to convolutional networks A Lagrange, B Le Saux, A Beaupere, A Boulch, A Chan-Hon-Tong, ... 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS …, 2015 | 104 | 2015 |
| Hard negative mining for metric learning based zero-shot classification M Bucher, S Herbin, F Jurie Computer Vision–ECCV 2016 Workshops: Amsterdam, The Netherlands, October 8 …, 2016 | 68 | 2016 |
| Robust abandoned object detection integrating wide area visual surveillance and social context J Ferryman, D Hogg, J Sochman, A Behera, JA Rodriguez-Serrano, ... Pattern Recognition Letters 34 (7), 789-798, 2013 | 54 | 2013 |
| Using hidden scale for salient object detection B Chalmond, B Francesconi, S Herbin IEEE Transactions on Image Processing 15 (9), 2644-2656, 2006 | 49 | 2006 |
| On-line fusion of trackers for single-object tracking I Leang, S Herbin, B Girard, J Droulez Pattern Recognition 74, 459-473, 2018 | 35 | 2018 |
| Semantic bottleneck for computer vision tasks M Bucher, S Herbin, F Jurie Computer Vision–ACCV 2018: 14th Asian Conference on Computer Vision, Perth …, 2019 | 16 | 2019 |
| Semi-supervised class incremental learning A Lechat, S Herbin, F Jurie 2020 25th International Conference on Pattern Recognition (ICPR), 10383-10389, 2021 | 11 | 2021 |
| Recognizing 3D objects by generating random actions S Herbin Proceedings CVPR IEEE Computer Society Conference on Computer Vision and …, 1996 | 11 | 1996 |
| Flexible tracklet association for complex scenarios using a markov logic network V Leung, S Herbin 2011 IEEE International Conference on Computer Vision Workshops (ICCV …, 2011 | 10 | 2011 |
| Context-driven moving object detection in aerial scenes with user input C Guilmart, S Herbin, P Perez Image Processing (ICIP), 2011 18th IEEE International Conference on, 1781-1784, 2011 | 10 | 2011 |
| Semantic lattices for multiple annotation of images AM Tousch, S Herbin, JY Audibert Proceedings of the 1st ACM international conference on Multimedia …, 2008 | 10 | 2008 |
| Robin challenge evaluation principles and metrics E D’Angelo, S Herbin, M Ratiéville see http://robin. inrialpes. fr, 2006 | 10 | 2006 |
| Zero-shot classification by generating artificial visual features M Bucher, S Herbin, F Jurie RFIAP, 2018 | 9 | 2018 |
| Exemplar based metric learning for robust visual localization C Le Barz, N Thome, M Cord, S Herbin, M Sanfourche 2015 IEEE International Conference on Image Processing (ICIP), 4342-4346, 2015 | 9 | 2015 |
| Adaptive planification in active 3d object recognition for many classes of objects J Defretin, S Herbin, G Le Besnerais, N Vayatis Workshop “Towards Closing the Loop: Active Learning for Robotics”, RSS …, 2010 | 8 | 2010 |
| Combining geometric and probabilistic structure for active recognition of 3D objects S Herbin Computer Vision—ECCV’98: 5th European Conference on Computer Vision …, 1998 | 8 | 1998 |
