Alex Kendall | DeepAI

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∙ 07/14/2023

Linking vision and motion for self-supervised object-centric perception

Object-centric representations enable autonomous driving algorithms to r...

0 Kaylene C. Stocking, et al. ∙

∙ 10/14/2022

Model-Based Imitation Learning for Urban Driving

An accurate model of the environment and the dynamic agents acting in it...

0 Anthony Hu, et al. ∙

∙ 08/12/2021

Reimagining an autonomous vehicle

The self driving challenge in 2021 is this century's technological equiv...

0 Jeffrey Hawke, et al. ∙

∙ 05/07/2021

Video Class Agnostic Segmentation with Contrastive Learning for Autonomous Driving

Semantic segmentation in autonomous driving predominantly focuses on lea...

5 Mennatullah Siam, et al. ∙

∙ 04/21/2021

FIERY: Future Instance Prediction in Bird's-Eye View from Surround Monocular Cameras

Driving requires interacting with road agents and predicting their futur...

10 Anthony Hu, et al. ∙

∙ 03/19/2021

Video Class Agnostic Segmentation Benchmark for Autonomous Driving

Semantic segmentation approaches are typically trained on large-scale da...

5 Mennatullah Siam, et al. ∙

∙ 03/13/2020

Probabilistic Future Prediction for Video Scene Understanding

We present a novel deep learning architecture for probabilistic future p...

7 Anthony Hu, et al. ∙

∙ 12/19/2019

Learning a Spatio-Temporal Embedding for Video Instance Segmentation

We present a novel embedding approach for video instance segmentation. O...

13 Anthony Hu, et al. ∙

∙ 11/30/2019

Urban Driving with Conditional Imitation Learning

Hand-crafting generalised decision-making rules for real-world urban aut...

0 Jeffrey Hawke, et al. ∙

∙ 12/10/2018

Learning to Drive from Simulation without Real World Labels

Simulation can be a powerful tool for understanding machine learning sys...

12 Alex Bewley, et al. ∙

∙ 11/20/2018

Orthographic Feature Transform for Monocular 3D Object Detection

3D object detection from monocular images has proven to be an enormously...

6 Thomas Roddick, et al. ∙

∙ 07/01/2018

Learning to Drive in a Day

We demonstrate the first application of deep reinforcement learning to a...

4 Alex Kendall, et al. ∙

∙ 05/22/2017

Concrete Dropout

Dropout is used as a practical tool to obtain uncertainty estimates in l...

0 Yarin Gal, et al. ∙

∙ 05/19/2017

Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics

Numerous deep learning applications benefit from multi-task learning wit...

0 Alex Kendall, et al. ∙

∙ 04/02/2017

Geometric Loss Functions for Camera Pose Regression with Deep Learning

Deep learning has shown to be effective for robust and real-time monocul...

0 Alex Kendall, et al. ∙

∙ 03/15/2017

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

There are two major types of uncertainty one can model. Aleatoric uncert...

0 Alex Kendall, et al. ∙

∙ 03/13/2017

End-to-End Learning of Geometry and Context for Deep Stereo Regression

We propose a novel deep learning architecture for regressing disparity f...

0 Alex Kendall, et al. ∙

∙ 11/09/2015

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

We present a deep learning framework for probabilistic pixel-wise semant...

0 Alex Kendall, et al. ∙

∙ 11/02/2015

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

We present a novel and practical deep fully convolutional neural network...

0 Vijay Badrinarayanan, et al. ∙

∙ 05/27/2015

PoseNet: A Convolutional Network for Real-Time 6-DOF Camera Relocalization

We present a robust and real-time monocular six degree of freedom reloca...

0 Alex Kendall, et al. ∙

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