research
∙
07/14/2023
Linking vision and motion for self-supervised object-centric perception
Object-centric representations enable autonomous driving algorithms to r...
share
research
∙
10/14/2022
Model-Based Imitation Learning for Urban Driving
An accurate model of the environment and the dynamic agents acting in it...
research
∙
08/12/2021
Reimagining an autonomous vehicle
The self driving challenge in 2021 is this century's technological equiv...
research
∙
05/07/2021
Video Class Agnostic Segmentation with Contrastive Learning for Autonomous Driving
Semantic segmentation in autonomous driving predominantly focuses on lea...
research
∙
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...
research
∙
03/19/2021
Video Class Agnostic Segmentation Benchmark for Autonomous Driving
Semantic segmentation approaches are typically trained on large-scale da...
research
∙
03/13/2020
Probabilistic Future Prediction for Video Scene Understanding
We present a novel deep learning architecture for probabilistic future p...
research
∙
12/19/2019
Learning a Spatio-Temporal Embedding for Video Instance Segmentation
We present a novel embedding approach for video instance segmentation. O...
research
∙
11/30/2019
Urban Driving with Conditional Imitation Learning
Hand-crafting generalised decision-making rules for real-world urban aut...
research
∙
12/10/2018
Learning to Drive from Simulation without Real World Labels
Simulation can be a powerful tool for understanding machine learning sys...
research
∙
11/20/2018
Orthographic Feature Transform for Monocular 3D Object Detection
3D object detection from monocular images has proven to be an enormously...
research
∙
07/01/2018
Learning to Drive in a Day
We demonstrate the first application of deep reinforcement learning to a...
research
∙
05/22/2017
Concrete Dropout
Dropout is used as a practical tool to obtain uncertainty estimates in l...
research
∙
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...
research
∙
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...
research
∙
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...
research
∙
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...
research
∙
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...
research
∙
11/02/2015
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
We present a novel and practical deep fully convolutional neural network...
research
∙
05/27/2015