33 citations · 101 across the 7 of their papers we have counts for
8 papers
GAIA-1: A Generative World Model for Autonomous Driving
Anthony Hu, Lloyd Russell, Hudson Yeo +5
Autonomous driving promises transformative improvements to transportation, but building systems capable of safely navigating the unstructured complexity of real-world scenarios rem…
Linking vision and motion for self-supervised object-centric perception
Kaylene C. Stocking, Zak Murez, Vijay Badrinarayanan +4
Object-centric representations enable autonomous driving algorithms to reason about interactions between many independent agents and scene features. Traditionally these representat…
Model-Based Imitation Learning for Urban Driving
Anthony Hu, Gianluca Corrado, Nicolas Griffiths +6
An accurate model of the environment and the dynamic agents acting in it offers great potential for improving motion planning. We present MILE: a Model-based Imitation LEarning app…
FIERY: Future Instance Prediction in Bird's-Eye View from Surround Monocular Cameras
Anthony Hu, Zak Murez, Nikhil Mohan +5
Driving requires interacting with road agents and predicting their future behaviour in order to navigate safely. We present FIERY: a probabilistic future prediction model in bird's…
Atlas: End-to-End 3D Scene Reconstruction from Posed Images
Zak Murez, Tarrence van As, James Bartolozzi +3
We present an end-to-end 3D reconstruction method for a scene by directly regressing a truncated signed distance function (TSDF) from a set of posed RGB images. Traditional approac…
DELTAS: Depth Estimation by Learning Triangulation And densification of Sparse points
Ayan Sinha, Zak Murez, James Bartolozzi +2
Multi-view stereo (MVS) is the golden mean between the accuracy of active depth sensing and the practicality of monocular depth estimation. Cost volume based approaches employing 3…
Zero-Shot Image Classification Using Coupled Dictionary Embedding
Mohammad Rostami, Soheil Kolouri, Zak Murez +3
Zero-shot learning (ZSL) is a framework to classify images belonging to unseen classes based on solely semantic information about these unseen classes. In this paper, we propose a…
Image to Image Translation for Domain Adaptation
Zak Murez, Soheil Kolouri, David Kriegman +2
We propose a general framework for unsupervised domain adaptation, which allows deep neural networks trained on a source domain to be tested on a different target domain without re…