A
Alex Kendall
Researcher at University of Cambridge
Publications - 45
Citations - 28781
Alex Kendall is an academic researcher from University of Cambridge. The author has contributed to research in topics: Deep learning & Convolutional neural network. The author has an hindex of 28, co-authored 42 publications receiving 19448 citations. Previous affiliations of Alex Kendall include University of Auckland.
Papers
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Journal ArticleDOI
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
TL;DR: 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.
Posted Content
What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision
Alex Kendall,Yarin Gal +1 more
TL;DR: 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.
Proceedings ArticleDOI
PoseNet: A Convolutional Network for Real-Time 6-DOF Camera Relocalization
TL;DR: PoseNet as mentioned in this paper uses a CNN 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.
Proceedings ArticleDOI
Multi-task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics
TL;DR: In this article, the authors make the observation that the performance of multi-task learning is strongly dependent on the relative weighting between each task's loss, and propose a principled approach to weight multiple loss functions by considering the homoscedastic uncertainty of each task.
Proceedings Article
What uncertainties do we need in Bayesian deep learning for computer vision
Alex Kendall,Yarin Gal +1 more
TL;DR: In this paper, a Bayesian deep learning framework combining input-dependent aleatoric uncertainty together with epistemic uncertainty was proposed for semantic segmentation and depth regression tasks, which can be interpreted as learned attenuation.