2.9k citations · 4.2k across the 10 of their papers we have counts for
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Geometric Loss Functions for Camera Pose Regression with Deep Learning
Alex Kendall, Roberto Cipolla
Deep learning has shown to be effective for robust and real-time monocular image relocalisation. In particular, PoseNet is a deep convolutional neural network which learns to regre…
Concrete Dropout
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. But to obtain well-calibrated uncertainty estimate…
Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics
Alex Kendall, Yarin Gal, Roberto Cipolla
Numerous deep learning applications benefit from multi-task learning with multiple regression and classification objectives. In this paper we make the observation that the performa…
End-to-End Learning of Geometry and Context for Deep Stereo Regression
Alex Kendall, Hayk Martirosyan, Saumitro Dasgupta +4
We propose a novel deep learning architecture for regressing disparity from a rectified pair of stereo images. We leverage knowledge of the problem's geometry to form a cost volume…
What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?
Alex Kendall, Yarin Gal
There are two major types of uncertainty one can model. Aleatoric uncertainty captures noise inherent in the observations. On the other hand, epistemic uncertainty accounts for unc…