| SegNet: A deep convolutional encoder-decoder architecture for scene segmentation V Badrinarayanan, A Kendall, R Cipolla IEEE transactions on pattern analysis and machine intelligence, 2017 | 22613 | 2017 |
| What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision? A Kendall, Y Gal Advances in Neural Information Processing Systems, 2017 | 6096 | 2017 |
| Multi-task learning using uncertainty to weigh losses for scene geometry and semantics A Kendall, Y Gal, R Cipolla Proceedings of the IEEE Conf. on Computer Vision and Pattern Recognition, 2018 | 3957 | 2018 |
| PoseNet: A Convolutional Network for Real-Time 6-DOF Camera Relocalization A Kendall, M Grimes, R Cipolla Proceedings of the IEEE International Conference on Computer Vision, 2015 | 2907 | 2015 |
| End-to-End Learning of Geometry and Context for Deep Stereo Regression A Kendall, H Martirosyan, S Dasgupta, P Henry, R Kennedy, A Bachrach, ... Proceedings of the IEEE International Conference on Computer Vision, 2017 | 1754* | 2017 |
| Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understanding. A Kendall, V Badrinarayanan, R Cipolla Proceedings of the British Machine Vision Conference, 2017 | 1473 | 2017 |
| Geometric loss functions for camera pose regression with deep learning A Kendall, R Cipolla Proceedings of the IEEE Conf. on Computer Vision and Pattern Recognition, 2017 | 949 | 2017 |
| Learning to Drive in a Day A Kendall, J Hawke, D Janz, P Mazur, D Reda, JM Allen, VD Lam, ... Proceedings of the International Conference on Robotics and Automation (ICRA), 2019 | 908 | 2019 |
| Concrete Dropout Y Gal, J Hron, A Kendall Advances in Neural Information Processing Systems, 2017 | 782 | 2017 |
| Modelling Uncertainty in Deep Learning for Camera Relocalization A Kendall, R Cipolla Proceedings of the IEEE International Conference on Robotics and Automation 2016, 2015 | 675 | 2015 |
| Orthographic feature transform for monocular 3d object detection T Roddick, A Kendall, R Cipolla Proceedings of the British Machine Vision Conference (BMVC), 2019 | 438 | 2019 |
| Concrete problems for autonomous vehicle safety: Advantages of Bayesian deep learning RT McAllister, Y Gal, A Kendall, M Van Der Wilk, A Shah, R Cipolla, ... International Joint Conferences on Artificial Intelligence, Inc., 2017 | 382 | 2017 |
| Fiery: Future instance prediction in bird's-eye view from surround monocular cameras A Hu, Z Murez, N Mohan, S Dudas, J Hawke, V Badrinarayanan, ... Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2021 | 309 | 2021 |
| Gaia-1: A generative world model for autonomous driving A Hu, L Russell, H Yeo, Z Murez, G Fedoseev, A Kendall, J Shotton, ... arXiv preprint arXiv:2309.17080, 2023 | 189 | 2023 |
| Urban driving with conditional imitation learning J Hawke, R Shen, C Gurau, S Sharma, D Reda, N Nikolov, P Mazur, ... 2020 IEEE International Conference on Robotics and Automation (ICRA), 251-257, 2020 | 189 | 2020 |
| Object tracking by an unmanned aerial vehicle using visual sensors S Dasgupta, H Martirosyan, H Koppula, A Kendall, A Stone, M Donahoe, ... US Patent 11,295,458, 2022 | 177 | 2022 |
| Model-based imitation learning for urban driving A Hu, G Corrado, N Griffiths, Z Murez, C Gurau, H Yeo, A Kendall, ... Advances in Neural Information Processing Systems 35, 20703-20716, 2022 | 144 | 2022 |
| Learning to Drive from Simulation without Real World Labels A Bewley, J Rigley, Y Liu, J Hawke, R Shen, VD Lam, A Kendall Proceedings of the International Conference on Robotics and Automation (ICRA), 2019 | 141 | 2019 |
| Probabilistic future prediction for video scene understanding A Hu, F Cotter, N Mohan, C Gurau, A Kendall Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23 …, 2020 | 94 | 2020 |
| On-board object tracking control of a quadcopter with monocular vision AG Kendall, NN Salvapantula, KA Stol 2014 international conference on unmanned aircraft systems (ICUAS), 404-411, 2014 | 93 | 2014 |
Roberto CipollaProfessor of Information Engineering, University of CambridgeE-mail confirmado em eng.cam.ac.uk