• Publications
  • Influence
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
Quantitative assessments show that SegNet provides good performance with competitive inference time and most efficient inference memory-wise as compared to other architectures, including FCN and DeconvNet.
What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?
A Bayesian deep learning framework combining input-dependent aleatoric uncertainty together with epistemic uncertainty is presented, which makes the loss more robust to noisy data, also giving new state-of-the-art results on segmentation and depth regression benchmarks.
PoseNet: A Convolutional Network for Real-Time 6-DOF Camera Relocalization
This work trains a convolutional neural network to regress the 6-DOF camera pose from a single RGB image in an end-to-end manner with no need of additional engineering or graph optimisation, demonstrating that convnets can be used to solve complicated out of image plane regression problems.
End-to-End Learning of Geometry and Context for Deep Stereo Regression
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 using deep feature
Multi-task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics
A principled approach to multi-task deep learning is proposed which weighs multiple loss functions by considering the homoscedastic uncertainty of each task, allowing us to simultaneously learn various quantities with different units or scales in both classification and regression settings.
Geometric Loss Functions for Camera Pose Regression with Deep Learning
  • Alex Kendall, R. Cipolla
  • Mathematics, Computer Science
    IEEE Conference on Computer Vision and Pattern…
  • 27 February 2017
A number of novel loss functions for learning camera pose which are based on geometry and scene reprojection error are explored, and it is shown how to automatically learn an optimal weighting to simultaneously regress position and orientation.
Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understanding
A practical system which is able to predict pixel-wise class labels with a measure of model uncertainty, and shows that modelling uncertainty improves segmentation performance by 2-3% across a number of state of the art architectures such as SegNet, FCN and Dilation Network, with no additional parametrisation.
Modelling uncertainty in deep learning for camera relocalization
A Bayesian convolutional neural network is used to regress the 6-DOF camera pose from a single RGB image and an estimate of the model's relocalization uncertainty is obtained to improve state of the art localization accuracy on a large scale outdoor dataset.
Concrete Dropout
This work proposes a new dropout variant which gives improved performance and better calibrated uncertainties, and uses a continuous relaxation of dropout’s discrete masks to allow for automatic tuning of the dropout probability in large models, and as a result faster experimentation cycles.
Learning to Drive in a Day
This work demonstrates a new framework for autonomous driving which moves away from reliance on defined logical rules, mapping, and direct supervision and provides a general and easy to obtain reward: the distance travelled by the vehicle without the safety driver taking control.