| Learning color names for real-world applications J Van De Weijer, C Schmid, J Verbeek, D Larlus IEEE Transactions on Image Processing 18 (7), 1512-1523, 2009 | 745 | 2009 |
| Deep image retrieval: Learning global representations for image search A Gordo, J Almazán, J Revaud, D Larlus European conference on computer vision, 241-257, 2016 | 635 | 2016 |
| Assessing the aesthetic quality of photographs using generic image descriptors L Marchesotti, F Perronnin, D Larlus, G Csurka 2011 international conference on computer vision, 1784-1791, 2011 | 393 | 2011 |
| End-to-end learning of deep visual representations for image retrieval A Gordo, J Almazan, J Revaud, D Larlus International Journal of Computer Vision 124 (2), 237-254, 2017 | 348 | 2017 |
| The 2005 pascal visual object classes challenge M Everingham, A Zisserman, CKI Williams, L Van Gool, M Allan, ... Machine Learning Challenges Workshop, 117-176, 2005 | 309 | 2005 |
| What is a good evaluation measure for semantic segmentation? G Csurka, D Larlus, F Perronnin British Machine Vision Conference, 2013 | 290 | 2013 |
| Weakly supervised recognition of daily life activities with wearable sensors M Stikic, D Larlus, S Ebert, B Schiele IEEE transactions on pattern analysis and machine intelligence 33 (12), 2521 …, 2011 | 185 | 2011 |
| Fisher vectors meet neural networks: A hybrid classification architecture F Perronnin, D Larlus Proceedings of the IEEE conference on computer vision and pattern …, 2015 | 173 | 2015 |
| Learning saliency maps for object categorization F Moosmann, D Larlus, F Jurie International Workshop on The Representation and Use of Prior Knowledge in …, 2006 | 113 | 2006 |
| Combining appearance models and markov random fields for category level object segmentation D Larlus, F Jurie 2008 IEEE Conference on Computer Vision and Pattern Recognition, 1-7, 2008 | 103 | 2008 |
| Re-id done right: towards good practices for person re-identification J Almazan, B Gajic, N Murray, D Larlus arXiv preprint arXiv:1801.05339, 2018 | 86 | 2018 |
| Category Level Object segmentation by Combining Bag of Words Models with Dirichlet Processes and Random Fields D Larlus, J Verbeek, F Jurie International Journal of Computer Vision 88 (2), 2010 | 77* | 2010 |
| Multi-graph based semi-supervised learning for activity recognition M Stikic, D Larlus, B Schiele 2009 international symposium on wearable computers, 85-92, 2009 | 75 | 2009 |
| Semi-convolutional Operators for Instance Segmentation D Novotny, S Albanie, D Larlus, A Vedaldi Proceedings of the European Conference on Computer Vision (ECCV), 2018 | 72 | 2018 |
| Latent mixture vocabularies for object categorization D Larlus, F Jurie The 17th British Machine Vision Conference (BVMC'06) 3, 959-968, 2006 | 72 | 2006 |
| Data driven localization using task-dependent representations JA Rodriguez-Serrano, D Larlus-Larrondo US Patent 9,158,995, 2015 | 64 | 2015 |
| Learning 3D Object Categories by Looking Around Them D Novotny, D Larlus, A Vedaldi Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2017 | 60 | 2017 |
| Beyond instance-level image retrieval: Leveraging captions to learn a global visual representation for semantic retrieval A Gordo, D Larlus Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017 | 58 | 2017 |
| Latent mixture vocabularies for object categorization and segmentation D Larlus, F Jurie Image and Vision Computing 27 (5), 523-534, 2009 | 57 | 2009 |
| AnchorNet: A Weakly Supervised Network to Learn Geometry-sensitive Features For Semantic Matching D Novotny, D Larlus, A Vedaldi Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2017 | 47 | 2017 |
Gabriela CsurkaPrincipal Scientist at Naver Labs EuropeVerified email at naverlabs.com
