| Associative hierarchical crfs for object class image segmentation L Ladicky, C Russell, P Kohli, PHS Torr Computer Vision, 2009 IEEE 12th International Conference on, 739-746, 2009 | 670 | 2009 |
| Graph cut based inference with co-occurrence statistics L Ladicky, C Russell, P Kohli, P Torr Computer Vision–ECCV 2010, 239-253, 2010 | 389 | 2010 |
| What, where and how many? combining object detectors and crfs Ľ Ladickż, P Sturgess, K Alahari, C Russell, PHS Torr Computer Vision–ECCV 2010, 424-437, 2010 | 370 | 2010 |
| Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR S Wachter, B Mittelstadt, C Russell Harvard Journal of Law & Technology 31 (2), 2018 | 295 | 2018 |
| Counterfactual fairness MJ Kusner, J Loftus, C Russell, R Silva Advances in Neural Information Processing Systems, 4066-4076, 2017 | 294 | 2017 |
| Lifting from the deep: Convolutional 3d pose estimation from a single image D Tome, C Russell, L Agapito Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2017 | 252 | 2017 |
| Joint optimization for object class segmentation and dense stereo reconstruction L Ladickż, P Sturgess, C Russell, S Sengupta, Y Bastanlar, W Clocksin, ... International Journal of Computer Vision 100 (2), 122-133, 2012 | 199 | 2012 |
| Veegan: Reducing mode collapse in gans using implicit variational learning A Srivastava, L Valkov, C Russell, MU Gutmann, C Sutton Advances in Neural Information Processing Systems, 3308-3318, 2017 | 158 | 2017 |
| Looking beyond the image: Unsupervised learning for object saliency and detection P Siva, C Russell, T Xiang, L Agapito Proceedings of the IEEE conference on computer vision and pattern …, 2013 | 147 | 2013 |
| In Defence of Negative Mining for Annotating Weakly Labelled Data P Siva, C Russell, T Xiang ECCV, 2012 | 125 | 2012 |
| Associative hierarchical random fields Ľ Ladickż, C Russell, P Kohli, PHS Torr IEEE Transactions on Pattern Analysis and Machine Intelligence 36 (6), 1056-1077, 2013 | 94 | 2013 |
| F-formation detection: Individuating free-standing conversational groups in images F Setti, C Russell, C Bassetti, M Cristani PloS one 10 (5), e0123783, 2015 | 82 | 2015 |
| Explaining explanations in AI B Mittelstadt, C Russell, S Wachter Proceedings of the conference on fairness, accountability, and transparency …, 2019 | 77 | 2019 |
| Video pop-up: Monocular 3d reconstruction of dynamic scenes C Russell, R Yu, L Agapito European conference on computer vision, 583-598, 2014 | 76 | 2014 |
| Energy based multiple model fitting for non-rigid structure from motion C Russell, J Fayad, L Agapito Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on …, 2011 | 72 | 2011 |
| Inference methods for crfs with co-occurrence statistics Ľ Ladickż, C Russell, P Kohli, PHS Torr International journal of computer vision 103 (2), 213-225, 2013 | 66 | 2013 |
| Direct, dense, and deformable: Template-based non-rigid 3d reconstruction from rgb video R Yu, C Russell, NDF Campbell, L Agapito Proceedings of the IEEE International Conference on Computer Vision, 918-926, 2015 | 58 | 2015 |
| Automated Articulated Structure and 3D Shape Recovery from Point Correspondences J Fayad, C Russell, L Agapito ICCV, 2011 | 50 | 2011 |
| Semi-supervised learning using an unsupervised atlas N Pitelis, C Russell, L Agapito Joint European Conference on Machine Learning and Knowledge Discovery in …, 2014 | 44 | 2014 |
| When worlds collide: integrating different counterfactual assumptions in fairness C Russell, MJ Kusner, J Loftus, R Silva Advances in Neural Information Processing Systems, 6414-6423, 2017 | 43 | 2017 |
Philip TorrProfessor, University of OxfordVerified email at eng.ox.ac.uk
