98 citations · 115 across the 8 of their papers we have counts for
11 papers
Structured SIR: Efficient and Expressive Importance-Weighted Inference for High-Dimensional Image Registration
Ivor J. A. Simpson, Neill D. F. Campbell
Image registration is an ill-posed dense vision task, where multiple solutions achieve similar loss values, motivating probabilistic inference. Variational inference has previously…
Structured Uncertainty Similarity Score (SUSS): Learning a Probabilistic, Interpretable, Perceptual Metric Between Images
Paula Seidler, Neill D. F. Campbell, Ivor J A Simpson
Perceptual similarity scores that align with human vision are critical for both training and evaluating computer vision models. Deep perceptual losses, such as LPIPS, achieve good…
HyperPredict: Estimating Hyperparameter Effects for Instance-Specific Regularization in Deformable Image Registration
Aisha L. Shuaibu, Ivor J. A. Simpson
Methods for medical image registration infer geometric transformations that align pairs/groups of images by maximising an image similarity metric. This problem is ill-posed as seve…
The Robust Semantic Segmentation UNCV2023 Challenge Results
Xuanlong Yu, Yi Zuo, Zitao Wang +34
This paper outlines the winning solutions employed in addressing the MUAD uncertainty quantification challenge held at ICCV 2023. The challenge was centered around semantic segment…
Compressed Sensing MRI Reconstruction Regularized by VAEs with Structured Image Covariance
Margaret Duff, Ivor J. A. Simpson, Matthias J. Ehrhardt +1
Objective: This paper investigates how generative models, trained on ground-truth images, can be used \changes{as} priors for inverse problems, penalizing reconstructions far from…
Flexible Amortized Variational Inference in qBOLD MRI
Ivor J. A. Simpson, Ashley McManamon, Balázs Örzsik +5
Streamlined qBOLD acquisitions enable experimentally straightforward observations of brain oxygen metabolism. maps are easily inferred; however, the Oxygen extraction…
Learning Structured Gaussians to Approximate Deep Ensembles
Ivor J. A. Simpson, Sara Vicente, Neill D. F. Campbell
This paper proposes using a sparse-structured multivariate Gaussian to provide a closed-form approximator for the output of probabilistic ensemble models used for dense image predi…
The GAN that Warped: Semantic Attribute Editing with Unpaired Data
Gara Dorta, Sara Vicente, Neill D. F. Campbell +1
Deep neural networks have recently been used to edit images with great success, in particular for faces. However, they are often limited to only being able to work at a restricted…
Estimation of Large Motion in Lung CT by Integrating Regularized Keypoint Correspondences into Dense Deformable Registration
Jan Rühaak, Thomas Polzin, Stefan Heldmann +4
We present a novel algorithm for the registration of pulmonary CT scans. Our method is designed for large respiratory motion by integrating sparse keypoint correspondences into a d…
Training VAEs Under Structured Residuals
Gara Dorta, Sara Vicente, Lourdes Agapito +2
Variational auto-encoders (VAEs) are a popular and powerful deep generative model. Previous works on VAEs have assumed a factorized likelihood model, whereby the output uncertainty…
Structured Uncertainty Prediction Networks
Gara Dorta, Sara Vicente, Lourdes Agapito +2
This paper is the first work to propose a network to predict a structured uncertainty distribution for a synthesized image. Previous approaches have been mostly limited to predicti…