| The Lovász-Softmax loss: A tractable surrogate for the optimization of the intersection-over-union measure in neural networks M Berman, A Rannen Triki, MB Blaschko | 956 | 2018 |
| Optimizing the dice score and jaccard index for medical image segmentation: Theory and practice J Bertels, T Eelbode, M Berman, D Vandermeulen, F Maes, R Bisschops, ... Medical Image Computing and Computer Assisted Intervention–MICCAI 2019: 22nd …, 2019 | 343 | 2019 |
| Optimization for medical image segmentation: theory and practice when evaluating with dice score or jaccard index T Eelbode, J Bertels, M Berman, D Vandermeulen, F Maes, R Bisschops, ... IEEE transactions on medical imaging 39 (11), 3679-3690, 2020 | 290 | 2020 |
| Multigrain: a unified image embedding for classes and instances M Berman, H Jégou, A Vedaldi, I Kokkinos, M Douze arXiv preprint arXiv:1902.05509, 2019 | 108 | 2019 |
| AOWS: Adaptive and optimal network width search with latency constraints M Berman, L Pishchulin, N Xu, MB Blaschko, G Medioni Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020 | 37 | 2020 |
| Optimization of the jaccard index for image segmentation with the lovász hinge M Berman, MB Blaschko CoRR, abs/1705.08790 5, 2017 | 35 | 2017 |
| A Bayesian Optimization Framework for Neural Network Compression X Ma, A Rannen Ep Triki, M Berman, C Sagonas, J Cali, MB Blaschko Proceedings of the IEEE International Conference on Computer Vision, 2019 | 26 | 2019 |
| Efficient semantic image segmentation with superpixel pooling M Schuurmans, M Berman, MB Blaschko arXiv preprint arXiv:1806.02705, 2018 | 22 | 2018 |
| Spatial consistency loss for training multi-label classifiers from single-label annotations T Verelst, PK Rubenstein, M Eichner, T Tuytelaars, M Berman Proceedings of the IEEE/CVF Winter Conference on Applications of Computer …, 2023 | 21 | 2023 |
| Adaptive compression-based lifelong learning S Srivastava, M Berman, MB Blaschko, D Tuia arXiv preprint arXiv:1907.09695, 2019 | 14 | 2019 |
| Function norms for neural networks A Rannen-Triki, M Berman, V Kolmogorov, MB Blaschko Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2019 | 7 | 2019 |
| Function norms and regularization in deep networks AR Triki, M Berman, MB Blaschko arXiv preprint arXiv:1710.06703, 2017 | 7* | 2017 |
| Generating superpixels using deep image representations T Verelst, M Blaschko, M Berman arXiv preprint arXiv:1903.04586, 2019 | 6 | 2019 |
| Revisiting evaluation metrics for semantic segmentation: Optimization and evaluation of fine-grained intersection over union Z Wang, M Berman, A Rannen-Triki, P Torr, D Tuia, T Tuytelaars, LV Gool, ... Advances in Neural Information Processing Systems 36, 2024 | 3 | 2024 |
| Generating superpixels with deep representations T Verelst, M Berman CVPR 2018 workshop on DeepVision: Beyond supervised learning, Date: 2018/06 …, 2018 | 3 | 2018 |
| Monocular surface reconstruction using 3D deformable part models S Kinauer, M Berman, I Kokkinos Computer Vision–ECCV 2016 Workshops: Amsterdam, The Netherlands, October 8 …, 2016 | 2 | 2016 |
| Supermodular Locality Sensitive Hashes M Berman, MB Blaschko arXiv preprint arXiv:1807.06686, 2018 | 1 | 2018 |
| Stochastic weighted function norm regularization AR Triki, M Berman, MB Blaschko CoRR, 2017 | 1 | 2017 |
| Slimmable neural network architecture search optimization M Berman, L Pishchulin, N Xu, GG Medioni US Patent 12,026,619, 2024 | | 2024 |
| Discriminative training of conditional random fields with probably submodular constraints M Berman, MB Blaschko International Journal of Computer Vision 128 (6), 1722-1735, 2020 | | 2020 |
Matthew BlaschkoKU LeuvenAdresse e-mail validée de esat.kuleuven.be
