| 3d semantic segmentation with submanifold sparse convolutional networks B Graham, M Engelcke, L Van Der Maaten Proceedings of the IEEE conference on computer vision and pattern …, 2018 | 1100 | 2018 |
| Sparse 3D convolutional neural networks B Graham BMVC 2015, 150.1-150.9, 2015 | 737* | 2015 |
| Fractional max-pooling B Graham arXiv preprint arXiv:1412.6071, 2014 | 640 | 2014 |
| Submanifold sparse convolutional networks B Graham, L Van der Maaten arXiv preprint arXiv:1706.01307, 2017 | 362 | 2017 |
| Spatially-sparse convolutional neural networks B Graham arXiv preprint arXiv:1409.6070, 2014 | 245 | 2014 |
| Levit: a vision transformer in convnet's clothing for faster inference B Graham, A El-Nouby, H Touvron, P Stock, A Joulin, H Jégou, M Douze Proceedings of the IEEE/CVF international conference on computer vision …, 2021 | 233 | 2021 |
| Training with quantization noise for extreme model compression A Fan, P Stock, B Graham, E Grave, R Gribonval, H Jegou, A Joulin arXiv preprint arXiv:2004.07320, 2020 | 160 | 2020 |
| Kaggle diabetic retinopathy detection competition report B Graham University of Warwick, 24-26, 2015 | 157 | 2015 |
| Exploring data-efficient 3d scene understanding with contrastive scene contexts J Hou, B Graham, M Nießner, S Xie Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2021 | 141 | 2021 |
| And the bit goes down: Revisiting the quantization of neural networks P Stock, A Joulin, R Gribonval, B Graham, H Jégou arXiv preprint arXiv:1907.05686, 2019 | 130 | 2019 |
| C3DPO: Canonical 3D Pose Networks for Non-Rigid Structure From Motion AV David Novotny, Nikhila Ravi, Benjamin Graham, Natalia Neverova ICCV, 2019 | 130* | 2019 |
| Automated plankton image analysis using convolutional neural networks RKC Jessica Y. Luo, Jean‐Olivier Irisson, Benjamin Graham, Cedric Guigand ... Limnology and Oceanography: Methods, 2018 | 107* | 2018 |
| Sparse arrays of signatures for online character recognition B Graham arXiv preprint arXiv:1308.0371, 2013 | 93 | 2013 |
| Influence and sharp-threshold theorems for monotonic measures BT Graham, GR Grimmett | 70 | 2006 |
| Large-scale 3d shape reconstruction and segmentation from shapenet core55 L Yi, L Shao, M Savva, H Huang, Y Zhou, Q Wang, B Graham, M Engelcke, ... arXiv preprint arXiv:1710.06104, 2017 | 66 | 2017 |
| Analyzing spatially-sparse data based on submanifold sparse convolutional neural networks BT Graham, LJP van der Maaten, MH Engelcke US Patent 11,544,550, 2023 | 52 | 2023 |
| Pri3d: Can 3d priors help 2d representation learning? J Hou, S Xie, B Graham, A Dai, M Nießner Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2021 | 43 | 2021 |
| 3d multi-bodies: Fitting sets of plausible 3d human models to ambiguous image data B Biggs, D Novotny, S Ehrhardt, H Joo, B Graham, A Vedaldi Advances in Neural Information Processing Systems 33, 20496-20507, 2020 | 42 | 2020 |
| The iisignature library: efficient calculation of iterated-integral signatures and log signatures J Reizenstein, B Graham https://arxiv.org/abs/1802.08252, 2018 | 39 | 2018 |
| Sharp thresholds for the random-cluster and Ising models B Graham, G Grimmett | 25 | 2011 |
