| 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 | 1827 | 2018 |
| Sparse 3D convolutional neural networks B Graham BMVC 2015, 150.1-150.9, 2015 | 919* | 2015 |
| 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 | 749 | 2021 |
| Fractional max-pooling B Graham arXiv preprint arXiv:1412.6071, 2014 | 743 | 2014 |
| Submanifold sparse convolutional networks B Graham, L Van der Maaten arXiv preprint arXiv:1706.01307, 2017 | 574 | 2017 |
| 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 | 315 | 2021 |
| Spatially-sparse convolutional neural networks B Graham arXiv preprint arXiv:1409.6070, 2014 | 308 | 2014 |
| Alaaeldin El-Nouby, Hugo Touvron, Pierre Stock, Armand Joulin, Hervé Jégou, and Matthijs Douze. Levit: a vision transformer in convnet’s clothing for faster inference B Graham Proceedings of the IEEE/CVF international conference on computer vision 2, 13, 2021 | 307 | 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 | 278 | 2020 |
| Kaggle diabetic retinopathy detection competition report B Graham University of Warwick 22 (9), 2015 | 247 | 2015 |
| Cotracker: It is better to track together N Karaev, I Rocco, B Graham, N Neverova, A Vedaldi, C Rupprecht European Conference on Computer Vision, 18-35, 2024 | 199 | 2024 |
| 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 | 182 | 2019 |
| C3DPO: Canonical 3D Pose Networks for Non-Rigid Structure From Motion AV David Novotny, Nikhila Ravi, Benjamin Graham, Natalia Neverova ICCV, 2019 | 171* | 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 | 161* | 2018 |
| Sparse arrays of signatures for online character recognition B Graham arXiv preprint arXiv:1308.0371, 2013 | 115 | 2013 |
| 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 | 88 | 2020 |
| 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 | 86 | 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 | 85 | 2021 |
| 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 | 74 | 2017 |
| Influence and sharp-threshold theorems for monotonic measures BT Graham, GR Grimmett | 74 | 2006 |
