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Alex Kendall
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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
186142017
What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?
A Kendall, Y Gal
Advances in Neural Information Processing Systems, 2017
48302017
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
30742018
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
24952015
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
1446*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
13172017
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
8352017
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
6652019
Concrete Dropout
Y Gal, J Hron, A Kendall
Advances in Neural Information Processing Systems, 2017
6622017
Modelling Uncertainty in Deep Learning for Camera Relocalization
A Kendall, R Cipolla
Proceedings of the IEEE International Conference on Robotics and Automation 2016, 2015
6172015
Orthographic feature transform for monocular 3d object detection
T Roddick, A Kendall, R Cipolla
Proceedings of the British Machine Vision Conference (BMVC), 2019
3532019
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
3172017
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
1812021
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
1312020
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
1282022
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
1192019
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
812014
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
742020
Concrete problems for autonomous vehicle safety: Advantages of bayesian deep learning. International Joint Conferences on Artificial Intelligence
R McAllister, Y Gal, A Kendall, M Van Der Wilk, A Shah, R Cipolla, ...
Inc, 2017
582017
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
522022
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