2 code implementations • 21 Dec 2023 • Ana-Maria Marcu, Long Chen, Jan Hünermann, Alice Karnsund, Benoit Hanotte, Prajwal Chidananda, Saurabh Nair, Vijay Badrinarayanan, Alex Kendall, Jamie Shotton, Elahe Arani, Oleg Sinavski
To fill this gap, we introduce LingoQA, a benchmark specifically for autonomous driving Video QA.
no code implementations • 29 Sep 2023 • Anthony Hu, Lloyd Russell, Hudson Yeo, Zak Murez, George Fedoseev, Alex Kendall, Jamie Shotton, Gianluca Corrado
Autonomous driving promises transformative improvements to transportation, but building systems capable of safely navigating the unstructured complexity of real-world scenarios remains challenging.
1 code implementation • 14 Jul 2023 • Kaylene C. Stocking, Zak Murez, Vijay Badrinarayanan, Jamie Shotton, Alex Kendall, Claire Tomlin, Christopher P. Burgess
Object-centric representations enable autonomous driving algorithms to reason about interactions between many independent agents and scene features.
1 code implementation • 14 Oct 2022 • Anthony Hu, Gianluca Corrado, Nicolas Griffiths, Zak Murez, Corina Gurau, Hudson Yeo, Alex Kendall, Roberto Cipolla, Jamie Shotton
Our approach is the first camera-only method that models static scene, dynamic scene, and ego-behaviour in an urban driving environment.
no code implementations • 12 Aug 2021 • Jeffrey Hawke, Haibo E, Vijay Badrinarayanan, Alex Kendall
The self driving challenge in 2021 is this century's technological equivalent of the space race, and is now entering the second major decade of development.
1 code implementation • 7 May 2021 • Mennatullah Siam, Alex Kendall, Martin Jagersand
Semantic segmentation in autonomous driving predominantly focuses on learning from large-scale data with a closed set of known classes without considering unknown objects.
1 code implementation • ICCV 2021 • Anthony Hu, Zak Murez, Nikhil Mohan, Sofía Dudas, Jeffrey Hawke, Vijay Badrinarayanan, Roberto Cipolla, Alex Kendall
We present FIERY: a probabilistic future prediction model in bird's-eye view from monocular cameras.
Ranked #1 on Bird's-Eye View Semantic Segmentation on nuScenes (IoU veh - 224x480 - No vis filter - 100x50 at 0.25 metric)
1 code implementation • 19 Mar 2021 • Mennatullah Siam, Alex Kendall, Martin Jagersand
We formalize the task of video class agnostic segmentation from monocular video sequences in autonomous driving to account for unknown objects.
no code implementations • ECCV 2020 • Anthony Hu, Fergal Cotter, Nikhil Mohan, Corina Gurau, Alex Kendall
We present a novel deep learning architecture for probabilistic future prediction from video.
1 code implementation • 19 Dec 2019 • Anthony Hu, Alex Kendall, Roberto Cipolla
We present a novel embedding approach for video instance segmentation.
no code implementations • 30 Nov 2019 • Jeffrey Hawke, Richard Shen, Corina Gurau, Siddharth Sharma, Daniele Reda, Nikolay Nikolov, Przemyslaw Mazur, Sean Micklethwaite, Nicolas Griffiths, Amar Shah, Alex Kendall
As our main contribution, we present an end-to-end conditional imitation learning approach, combining both lateral and longitudinal control on a real vehicle for following urban routes with simple traffic.
no code implementations • 10 Dec 2018 • Alex Bewley, Jessica Rigley, Yuxuan Liu, Jeffrey Hawke, Richard Shen, Vinh-Dieu Lam, Alex Kendall
Simulation can be a powerful tool for understanding machine learning systems and designing methods to solve real-world problems.
1 code implementation • 20 Nov 2018 • Thomas Roddick, Alex Kendall, Roberto Cipolla
This allows us to reason holistically about the spatial configuration of the scene in a domain where scale is consistent and distances between objects are meaningful.
3D Object Detection From Monocular Images Monocular 3D Object Detection +2
7 code implementations • 1 Jul 2018 • Alex Kendall, Jeffrey Hawke, David Janz, Przemyslaw Mazur, Daniele Reda, John-Mark Allen, Vinh-Dieu Lam, Alex Bewley, Amar Shah
We demonstrate the first application of deep reinforcement learning to autonomous driving.
5 code implementations • NeurIPS 2017 • Yarin Gal, Jiri Hron, Alex Kendall
Dropout is used as a practical tool to obtain uncertainty estimates in large vision models and reinforcement learning (RL) tasks.
16 code implementations • CVPR 2018 • Alex Kendall, Yarin Gal, Roberto Cipolla
Numerous deep learning applications benefit from multi-task learning with multiple regression and classification objectives.
1 code implementation • CVPR 2017 • Alex Kendall, Roberto Cipolla
Deep learning has shown to be effective for robust and real-time monocular image relocalisation.
11 code implementations • NeurIPS 2017 • Alex Kendall, Yarin Gal
On the other hand, epistemic uncertainty accounts for uncertainty in the model -- uncertainty which can be explained away given enough data.
Ranked #100 on Semantic Segmentation on NYU Depth v2
3 code implementations • ICCV 2017 • Alex Kendall, Hayk Martirosyan, Saumitro Dasgupta, Peter Henry, Ryan Kennedy, Abraham Bachrach, Adam Bry
We propose a novel deep learning architecture for regressing disparity from a rectified pair of stereo images.
22 code implementations • 9 Nov 2015 • Alex Kendall, Vijay Badrinarayanan, Roberto Cipolla
Semantic segmentation is an important tool for visual scene understanding and a meaningful measure of uncertainty is essential for decision making.
74 code implementations • 2 Nov 2015 • Vijay Badrinarayanan, Alex Kendall, Roberto Cipolla
We show that SegNet provides good performance with competitive inference time and more efficient inference memory-wise as compared to other architectures.
Ranked #3 on Medical Image Segmentation on RITE
1 code implementation • 19 Sep 2015 • Alex Kendall, Roberto Cipolla
Using a Bayesian convolutional neural network implementation we obtain an estimate of the model's relocalization uncertainty and improve state of the art localization accuracy on a large scale outdoor dataset.
6 code implementations • ICCV 2015 • Alex Kendall, Matthew Grimes, Roberto Cipolla
We present a robust and real-time monocular six degree of freedom relocalization system.