| Bootstrap Your Own Latent-A New Approach to Self-Supervised Learning JB Grill, F Strub, F Altché, C Tallec, P Richemond, E Buchatskaya, ... Advances in Neural Information Processing Systems 33, 2020 | 3470 | 2020 |
| Unsupervised visual representation learning by context prediction C Doersch, A Gupta, AA Efros Proceedings of the IEEE International Conference on Computer Vision, 1422-1430, 2015 | 2605 | 2015 |
| Tutorial on Variational Autoencoders C Doersch arXiv preprint arXiv:1606.05908, 2016 | 1794 | 2016 |
| Data-efficient image recognition with contrastive predictive coding OJ Hénaff, A Srinivas, J De Fauw, A Razavi, C Doersch, SM Eslami, ... arXiv preprint arXiv:1905.09272, 2019 | 1152 | 2019 |
| What makes Paris look like Paris? C Doersch, S Singh, A Gupta, J Sivic, AA Efros ACM Transactions on Graphics (SIGGRAPH) 31 (4), 101, 2012 | 775 | 2012 |
| Video action transformer network R Girdhar, J Carreira, C Doersch, A Zisserman Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2019 | 641 | 2019 |
| Multi-task self-supervised visual learning C Doersch, A Zisserman Proceedings of the IEEE International Conference on Computer Vision, 2051-2060, 2017 | 638 | 2017 |
| An Uncertain Future: Forecasting from Static Images Using Variational Autoencoders J Walker, C Doersch, A Gupta, M Hebert European Conference on Computer Vision, 835-851, 2016 | 558 | 2016 |
| Mid-level visual element discovery as discriminative mode seeking C Doersch, A Gupta, AA Efros Advances in neural information processing systems 26, 494-502, 2013 | 321 | 2013 |
| Perceiver IO: A General Architecture for Structured Inputs & Outputs A Jaegle, S Borgeaud, JB Alayrac, C Doersch, C Ionescu, D Ding, ... arXiv preprint arXiv:2107.14795, 2021 | 246 | 2021 |
| Data-dependent Initializations of Convolutional Neural Networks P Krähenbühl, C Doersch, J Donahue, T Darrell arXiv preprint arXiv:1511.06856, 2015 | 231 | 2015 |
| CrossTransformers: spatially-aware few-shot transfer C Doersch, A Gupta, A Zisserman Advances in Neural Information Processing Systems 33, 2020 | 212 | 2020 |
| Exploiting temporal context for 3D human pose estimation in the wild A Arnab, C Doersch, A Zisserman Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2019 | 197 | 2019 |
| Sim2real transfer learning for 3D human pose estimation: motion to the rescue C Doersch, A Zisserman Advances in Neural Information Processing Systems, 12949-12961, 2019 | 135 | 2019 |
| What makes Paris look like Paris? C Doersch, S Singh, A Gupta, J Sivic, AA Efros Communications of the ACM 58 (12), 103-110, 2015 | 119 | 2015 |
| Kickstarting Deep Reinforcement Learning S Schmitt, JJ Hudson, A Zidek, S Osindero, C Doersch, WM Czarnecki, ... arXiv preprint arXiv:1803.03835, 2018 | 109 | 2018 |
| Learning visual question answering by bootstrapping hard attention M Malinowski, C Doersch, A Santoro, P Battaglia Proceedings of the European Conference on Computer Vision (ECCV), 3-20, 2018 | 103 | 2018 |
| Structured agents for physical construction V Bapst, A Sanchez-Gonzalez, C Doersch, K Stachenfeld, P Kohli, ... International Conference on Machine Learning, 464-474, 2019 | 80 | 2019 |
| A Better Baseline for AVA R Girdhar, J Carreira, C Doersch, A Zisserman arXiv preprint arXiv:1807.10066, 2018 | 64 | 2018 |
| Context as supervisory signal: Discovering objects with predictable context C Doersch, A Gupta, AA Efros European Conference on Computer Vision, 362-377, 2014 | 43 | 2014 |
Andrew ZissermanUniversity of OxfordVerified email at robots.ox.ac.uk
