Publications

2023

Likelihood-based Out-of-Distribution Detection with Denoising Diffusion Probabilistic Models,
Joseph Goodier and Neill D. F. Campbell,
British Machine Vision Conf. (BMVC), 2023
[pdf] [supplemental]

Regularising Inverse Problems with Generative Machine Learning Models,
Margaret Duff, Neill D. F. Campbell and Matthias J. Ehrhardt,
Journal of Mathematical Imaging and Vision, 2023
[pdf] [arXiv link] [DOI: 10.1007/s10851-023-01162-x]

VAEs with Structured Image Covariance Applied to Compressed Sensing MRI,
Margaret Duff, Ivor Simpson, Matthias J. Ehrhardt and Neill D. F. Campbell,
IoP Physics in Medicine and Biology, vol. 68, no. 16, 2023
[pdf] [project page] [arXiv link] [DOI: 10.1088/1361-6560/ace49a]

2022

Analysing Training-Data Leakage from Gradients through Linear Systems and Gradient Matching,
Cangxiong Chen and Neill D. F. Campbell,
British Machine Vision Conf. (BMVC), 2022
[pdf] [code]

Cell Anomaly Localisation using Structured Uncertainty Prediction Networks,
Boyko Vodenicharski, Samuel McDermott, Katherine Webber, Viola Introini, Pietro Cicuta, Richard Bowman, Ivor Simpson and Neill D. F. Campbell,
Int. Conf. on Medical Imaging with Deep Learning (MIDL), 2022
[pdf] [code] [project page]

Learning Structured Gaussians to Approximate Deep Ensembles,
Ivor Simpson, Sara Vicente and Neill D. F. Campbell,
IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2022
[pdf] [supplemental] [project page]

Aligned Multi-Task Gaussian Process,
Olga Mikheeva, Ieva Kazlauskaite, Adam Hartshorne, Hedvig Kjellström, Carl Henrik Ek and Neill D. F. Campbell,
Int. Conf. on Artificial Intelligence and Statistics (AISTATS), 2022
[pdf] [code] [project page]

Shooting Schrödinger's Cat,
David Fernandes, Francisco Vargas, Carl Henrik Ek and Neill D. F. Campbell,
Symposium on Advances in Approximate Bayesian Inference (AABI), 2022
[pdf]

2021

Black-Box Density Function Estimation using Recursive Partitioning,
Erik Bodin, Zhenwen Dai, Neill D. F. Campbell and Carl Henrik Ek,
Int. Conf. on Machine Learning (ICML), 2021
[pdf] [supplemental] [code]

Active Latent Space Shape Model: A Bayesian Treatment of Shape Model Adaptation with an Application to Psoriatic Arthritis Radiographs,
Adwaye Rambojun, William Tillett, Tony Shardlow and Neill D. F. Campbell,
IEEE Winter Conf. on the Applications of Computer Vision (WACV), 2021
[pdf] [supplemental]

2020

Density Function Estimation using Ergodic Recursion,
Erik Bodin, Zhenwen Dai, Neill D. F. Campbell and Carl Henrik Ek,
Workshop on Machine Learning and the Physical Sciences at NeurIPS, 2020
[pdf]

Compositional Uncertainty in Deep Gaussian Processes,
Ivan Ustyuzhaninov, Ieva Kazlauskaite, Markus Kaiser, Erik Bodin, Neill D. F. Campbell and Carl Henrik Ek,
Conf. on Uncertainty in Artificial Intelligence (UAI), 2020
[pdf] [supplemental] [code]

Modulating Surrogates for Bayesian Optimization,
Erik Bodin, Markus Kaiser, Ieva Kazlauskaite, Zhenwen Dai, Neill D. F. Campbell and Carl Henrik Ek,
Int. Conf. on Machine Learning (ICML), 2020
[pdf] [supplemental]

Monotonic Gaussian Process Flow,
Ivan Ustyuzhaninov, Ieva Kazlauskaite, Carl Henrik Ek and Neill D. F. Campbell,
Int. Conf. on Artificial Intelligence and Statistics (AISTATS), 2020
[pdf] [code] [project page]

The GAN that Warped: Semantic Attribute Editing with Unpaired Data,
Garoe Dorta Perez, Sara Vicente, Neill D. F. Campbell and Ivor Simpson,
IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2020
[pdf] [supplemental]

2019

Compositional Uncertainty in Deep Gaussian Processes,
Ivan Ustyuzhaninov, Ieva Kazlauskaite, Markus Kaiser, Erik Bodin, Neill D. F. Campbell and Carl Henrik Ek,
Bayesian Deep Learning Workshop at NeurIPS, 2019
[pdf]

Fixing Implicit Derivatives: Trust-Region Based Learning of Continuous Energy Functions,
Matteo Toso, Neill D. F. Campbell and Chris Russell,
Conf. on Neural Information Processing Systems (NeurIPS), 2019
[pdf] [supplemental] [code] [video]

DP-GP-LVM: A Bayesian Non-Parametric Model for Learning Multivariate Dependency Structures,
Andrew Lawrence, Carl Henrik Ek and Neill D. F. Campbell,
Int. Conf. on Machine Learning (ICML), 2019
[pdf] [supplemental]

MegaParallax: Casual 360° Panoramas with Motion Parallax,
Tobias Bertel, Neill D. F. Campbell and Christian Richardt,
IEEE Transactions on Visualization and Computer Graphics, vol. 25, no. 5, 2019
[pdf] [project page]

Gaussian Process Latent Variable Alignment Learning,
Ieva Kazlauskaite, Carl Henrik Ek and Neill D. F. Campbell,
Int. Conf. on Artificial Intelligence and Statistics (AISTATS), 2019
[pdf] [code] [project page]

2018

Training VAEs Under Structured Residuals,
Garoe Dorta Perez, Sara Vicente, Lourdes Agapito, Neill D. F. Campbell and Ivor Simpson,
arXiv e-print, 2018
[pdf] [arXiv link]

Sequence Alignment with Dirichlet Process Mixtures,
Ieva Kazlauskaite, Ivan Ustyuzhaninov, Carl Henrik Ek and Neill D. F. Campbell,
NeurIPS Workshop on Bayesian Non-Parametrics, 2018
[pdf] [project page]

Gaussian Process Deep Belief Networks: A Smooth Generative Model of Shape with Uncertainty Propagation,
Alessandro Di Martino, Erik Bodin, Carl Henrik Ek and Neill D. F. Campbell,
Asian Conf. on Computer Vision (ACCV), 2018
[pdf] [code] [project page]

DiverseNet: When One Right Answer Is Not Enough,
Michael Firman, Neill D. F. Campbell, Lourdes Agapito and Gabriel J. Brostow,
IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2018
[pdf]

Structured Uncertainty Prediction Networks,
Garoe Dorta Perez, Sara Vicente, Lourdes Agapito, Neill D. F. Campbell and Ivor Simpson,
IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2018
[pdf] [supplemental] [code] [project page]

2017

Latent Gaussian Process Regression,
Erik Bodin, Neill D. F. Campbell and Carl Henrik Ek,
arXiv e-print, 2017
[pdf] [arXiv link]

Latent Structure Learning using Gaussian and Dirichlet Processes,
Andrew Lawrence, Carl Henrik Ek and Neill D. F. Campbell,
NeurIPS Workshop on Advances in Modelling and Learning Interactions from Complex Data, 2017
[pdf]

Nonparametric Inference for Auto-Encoding Variational Bayes,
Erik Bodin, Iman Malik, Carl Henrik Ek and Neill D. F. Campbell,
NeurIPS Workshop on Advances in Approximate Bayesian Inference, 2017
[pdf]

Laplacian Pyramid of Conditional Variational Autoencoders,
Garoe Dorta Perez, Sara Vicente, Lourdes Agapito, Neill D. F. Campbell, Simon Prince and Ivor Simpson,
European Conf. on Visual Media Production (CVMP), 2017
[pdf]

Responsive Action-based Video Synthesis,
Corneliu Ilisescu, Halil Aytac Kanaci, Matteo Romagnoli, Neill D. F. Campbell and Gabriel J. Brostow,
ACM CHI Conf. on Human Factors in Computing Systems, 2017
[pdf]

2016

Learning Alignments from Latent Space Structures,
Ieva Kazlauskaite, Carl Henrik Ek and Neill D. F. Campbell,
NeurIPS Workshop on Learning in High-Dimensions with Structure, 2016
[pdf] [project page]

Roto++: Accelerating Professional Rotoscoping using Shape Manifolds,
Wenbin Li, Fabio Viola, Jonathan Starck, Gabriel J. Brostow and Neill D.F. Campbell,
ACM Transactions on Graphics (SIGGRAPH), vol. 35, no. 4, 2016
[pdf] [supplemental] [code] [project page]

Reading Between the Dots: Combining 3D Markers and FACS Classification for High-Quality Blendshape Facial Animation,
Shridhar Ravikumar, Colin Davidson, Dmitry Kit, Neill D. F. Campbell, Luca Benedetti and Darren Cosker,
Graphics Interface (GI), 2016
[pdf]

2015

Fitting Quadrics with a Bayesian Prior,
Daniel Beale, Yong-Liang Yang, Darren Cosker, Neill D. F. Campbell and Peter Hall,
Computational Visual Media (CVM), vol. 2, no. 2, 2015
[pdf]

Dense, Direct and Deformable: Non-Rigid 3D Reconstruction from RGB Video,
Rui Yu, Chris Russell, Neill D. F. Campbell and Lourdes Agapito,
IEEE Int. Conf. on Computer Vision (ICCV), 2015
[pdf]

Modeling Object Appearance using Context-Conditioned Component Analysis,
Daniyar Turmukhambetov, Neill D. F. Campbell, Simon Prince and Jan Kautz,
IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2015
[pdf] [supplemental]

Interactive Sketch-Driven Image Synthesis,
Daniyar Turmukhambetov, Neill D. F. Campbell, Dan Goldman and Jan Kautz,
Computer Graphics Forum (CGF), vol. 34, no. 8, 2015
[pdf]

2014

Learning a Manifold of Fonts,
Neill D. F. Campbell and Jan Kautz,
ACM Transactions on Graphics (SIGGRAPH), vol. 33, no. 4, 2014
[pdf] [supplemental] [project page]

User Directed Multi-View Stereo,
Yotam Doron, Neill D. F. Campbell, Jon Starck and Jan Kautz,
ACCV Workshop on User-Centred Computer Vision, 2014
[pdf]

Hierarchical Subquery Evaluation for Active Learning on a Graph,
Oisin Mac Aodha, Neill D. F. Campbell, Jan Kautz and Gabriel J. Brostow,
IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2014
[pdf] [supplemental]

2013

Fully-Connected CRFs with Non-Parametric Pairwise Potentials,
Neill D. F. Campbell, Kartic Subr and Jan Kautz,
IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2013
[pdf] [supplemental]

2012

Patch Based Synthesis for Single Depth Image Super-Resolution,
Oisin Mac Aodha, Neill D. F. Campbell, Arun Nair and Gabriel J. Brostow,
European Conf. on Computer Vision (ECCV), 2012
[pdf]

2011

Automatic Object Segmentation from Calibrated Images,
Neill D. F. Campbell, George Vogiatzis, Carlos Hernández and Roberto Cipolla,
European Conf. on Visual Media Production (CVMP), 2011
[pdf] [project page]

2010

Automatic 3D Object Segmentation in Multiple Views using Volumetric Graph-Cuts,
Neill D. F. Campbell, George Vogiatzis, Carlos Hernández and Roberto Cipolla,
Image and Vision Computing, vol. 28, no. 1, 2010
[pdf] [project page]

2008

Using Multiple Hypotheses to Improve Depth-Maps for Multi-View Stereo,
Neill D. F. Campbell, George Vogiatzis, Carlos Hernández and Roberto Cipolla,
European Conf. on Computer Vision (ECCV), 2008
[pdf] [project page]

2007

Automatic 3D Object Segmentation in Multiple Views using Volumetric Graph-Cuts,
Neill D. F. Campbell, George Vogiatzis, Carlos Hernández and Roberto Cipolla,
British Machine Vision Conf. (BMVC), 2007
[pdf] [project page]