1 code implementation • ICCV 2023 • Abril Corona-Figueroa, Sam Bond-Taylor, Neelanjan Bhowmik, Yona Falinie A. Gaus, Toby P. Breckon, Hubert P. H. Shum, Chris G. Willcocks
Generating 3D images of complex objects conditionally from a few 2D views is a difficult synthesis problem, compounded by issues such as domain gap and geometric misalignment.
1 code implementation • 31 Mar 2023 • Sam Bond-Taylor, Chris G. Willcocks
This paper introduces $\infty$-Diff, a generative diffusion model defined in an infinite-dimensional Hilbert space, which can model infinite resolution data.
1 code implementation • CVPR 2023 • Brian K. S. Isaac-Medina, Chris G. Willcocks, Toby P. Breckon
In this paper, we explore the use of an exact approach for calculating the IPE by using a pyramid-based integral formulation instead of an approximated conical-based one.
1 code implementation • CVPR 2022 • Julian Wyatt, Adam Leach, Sebastian M. Schmon, Chris G. Willcocks
A secondary problem is that Gaussian diffusion fails to capture larger anomalies; therefore we develop a multi-scale simplex noise diffusion process that gives control over the target anomaly size.
Ranked #21 on Anomaly Detection on VisA
1 code implementation • 24 Jun 2022 • Alex F. McKinney, Chris G. Willcocks
An ongoing trend in generative modelling research has been to push sample resolutions higher whilst simultaneously reducing computational requirements for training and sampling.
2 code implementations • 2 Feb 2022 • Abril Corona-Figueroa, Jonathan Frawley, Sam Bond-Taylor, Sarath Bethapudi, Hubert P. H. Shum, Chris G. Willcocks
Computed tomography (CT) is an effective medical imaging modality, widely used in the field of clinical medicine for the diagnosis of various pathologies.
3 code implementations • 24 Nov 2021 • Sam Bond-Taylor, Peter Hessey, Hiroshi Sasaki, Toby P. Breckon, Chris G. Willcocks
Whilst diffusion probabilistic models can generate high quality image content, key limitations remain in terms of both generating high-resolution imagery and their associated high computational requirements.
Ranked #4 on Image Generation on LSUN Bedroom 256 x 256 (Recall metric)
no code implementations • 12 Apr 2021 • Hiroshi Sasaki, Chris G. Willcocks, Toby P. Breckon
Our method, UNpaired Image Translation with Denoising Diffusion Probabilistic Models (UNIT-DDPM), trains a generative model to infer the joint distribution of images over both domains as a Markov chain by minimising a denoising score matching objective conditioned on the other domain.
1 code implementation • 25 Mar 2021 • Brian K. S. Isaac-Medina, Matt Poyser, Daniel Organisciak, Chris G. Willcocks, Toby P. Breckon, Hubert P. H. Shum
Unmanned Aerial Vehicles (UAV) can pose a major risk for aviation safety, due to both negligent and malicious use.
no code implementations • 8 Mar 2021 • Sam Bond-Taylor, Adam Leach, Yang Long, Chris G. Willcocks
Deep generative models are a class of techniques that train deep neural networks to model the distribution of training samples.
no code implementations • 1 Mar 2021 • Jonathan Frawley, Chris G. Willcocks, Maged Habib, Caspar Geenen, David H. Steel, Boguslaw Obara
Macular holes are a common eye condition which result in visual impairment.
no code implementations • 5 Oct 2020 • Bao Nguyen, Adam Feldman, Sarath Bethapudi, Andrew Jennings, Chris G. Willcocks
Medical segmentation is performed to determine the bounds of regions of interest (ROI) prior to surgery.
4 code implementations • ICLR 2021 • Sam Bond-Taylor, Chris G. Willcocks
This paper proposes a new type of generative model that is able to quickly learn a latent representation without an encoder.
no code implementations • 10 May 2020 • Jonathan Frawley, Chris G. Willcocks, Maged Habib, Caspar Geenen, David H. Steel, Boguslaw Obara
This paper investigates the application of deep convolutional neural networks with prohibitively small datasets to the problem of macular edema segmentation.
no code implementations • 5 May 2020 • Hiroshi Sasaki, Chris G. Willcocks, Toby P. Breckon
However, such applications often suffer due to the limited quantity and variety of non-visible spectral domain imagery, in contrast to the high data availability of visible-band imagery that readily enables contemporary deep learning driven detection and classification approaches.
no code implementations • 10 Oct 2019 • Venkata K. Ramaswamy, Chris G. Willcocks, Matteo T. Degiacomi
Determining the different conformational states of a protein and the transition paths between them is key to fully understanding the relationship between biomolecular structure and function.
1 code implementation • 28 Apr 2019 • Fady Medhat, Mahnaz Mohammadi, Sardar Jaf, Chris G. Willcocks, Toby P. Breckon, Peter Matthews, Andrew Stephen McGough, Georgios Theodoropoulos, Boguslaw Obara
In this work, we present a generic process flow for text recognition in scanned documents containing mixed handwritten and machine-printed text without the need to classify text in advance.