| 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 | 6179 | 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 | 3185 | 2015 |
| Tutorial on Variational Autoencoders C Doersch arXiv preprint arXiv:1606.05908, 2016 | 2349 | 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 | 1507 | 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 | 985 | 2015 |
| Video action transformer network R Girdhar, J Carreira, C Doersch, A Zisserman Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2019 | 814 | 2019 |
| Multi-task self-supervised visual learning C Doersch, A Zisserman Proceedings of the IEEE International Conference on Computer Vision, 2051-2060, 2017 | 770 | 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 | 621 | 2016 |
| 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 | 485 | 2021 |
| CrossTransformers: spatially-aware few-shot transfer C Doersch, A Gupta, A Zisserman Advances in Neural Information Processing Systems 33, 2020 | 322 | 2020 |
| 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 | 318 | 2013 |
| 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 | 262 | 2019 |
| Data-dependent Initializations of Convolutional Neural Networks P Krähenbühl, C Doersch, J Donahue, T Darrell arXiv preprint arXiv:1511.06856, 2015 | 241 | 2015 |
| 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 | 173 | 2019 |
| Kickstarting Deep Reinforcement Learning S Schmitt, JJ Hudson, A Zidek, S Osindero, C Doersch, WM Czarnecki, ... arXiv preprint arXiv:1803.03835, 2018 | 140 | 2018 |
| Kubric: A scalable dataset generator K Greff, F Belletti, L Beyer, C Doersch, Y Du, D Duckworth, DJ Fleet, ... Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022 | 138 | 2022 |
| 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 | 119 | 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 | 110 | 2019 |
| A Better Baseline for AVA R Girdhar, J Carreira, C Doersch, A Zisserman arXiv preprint arXiv:1807.10066, 2018 | 71 | 2018 |
| TAP-Vid: A Benchmark for Tracking Any Point in a Video C Doersch, A Gupta, L Markeeva, AR Continente, L Smaira, Y Aytar, ... Neural Information Processing Systems Datasets and Benchmarks Track, 2022 | 70 | 2022 |
Andrew ZissermanUniversity of Oxford在 robots.ox.ac.uk 的电子邮件经过验证
