Search Results for author: Alex Kendall

Found 19 papers, 15 papers with code

Reimagining an autonomous vehicle

no code implementations12 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.

Autonomous Driving

Video Class Agnostic Segmentation with Contrastive Learning for Autonomous Driving

1 code implementation7 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.

Autonomous Driving Contrastive Learning +1

Video Class Agnostic Segmentation Benchmark for Autonomous Driving

1 code implementation19 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.

Autonomous Driving Instance Segmentation +2

Urban Driving with Conditional Imitation Learning

no code implementations30 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.

Autonomous Driving Decision Making +2

Learning to Drive from Simulation without Real World Labels

no code implementations10 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.

Image-to-Image Translation Translation

Orthographic Feature Transform for Monocular 3D Object Detection

1 code implementation20 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 +1

Concrete Dropout

4 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.

Geometric Loss Functions for Camera Pose Regression with Deep Learning

1 code implementation CVPR 2017 Alex Kendall, Roberto Cipolla

Deep learning has shown to be effective for robust and real-time monocular image relocalisation.

What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?

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.

Depth Estimation Semantic Segmentation

Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understanding

24 code implementations9 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.

Decision Making Scene Understanding +1

SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation

74 code implementations2 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.

Crowd Counting General Classification +5

Modelling Uncertainty in Deep Learning for Camera Relocalization

1 code implementation19 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.

Camera Relocalization

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