27 citations · 105 across the 13 of their papers we have counts for
14 papers
Multi-Scale Cross Contrastive Learning for Semi-Supervised Medical Image Segmentation
Qianying Liu, Xiao Gu, Paul Henderson +1
Semi-supervised learning has demonstrated great potential in medical image segmentation by utilizing knowledge from unlabeled data. However, most existing approaches do not explici…
Simulating analogue film damage to analyse and improve artefact restoration on high-resolution scans
Daniela Ivanova, John Williamson, Paul Henderson
Digital scans of analogue photographic film typically contain artefacts such as dust and scratches. Automated removal of these is an important part of preservation and disseminatio…
RenderDiffusion: Image Diffusion for 3D Reconstruction, Inpainting and Generation
Titas Anciukevičius, Zexiang Xu, Matthew Fisher +4
Diffusion models currently achieve state-of-the-art performance for both conditional and unconditional image generation. However, so far, image diffusion models do not support task…
Deep learning extraction of band structure parameters from density of states: a case study on trilayer graphene
Paul Henderson, Areg Ghazaryan, Alexander A. Zibrov +2
The development of two-dimensional materials has resulted in a diverse range of novel, high-quality compounds with increasing complexity. A key requirement for a comprehensive quan…
Unsupervised Video Prediction from a Single Frame by Estimating 3D Dynamic Scene Structure
Paul Henderson, Christoph H. Lampert, Bernd Bickel
Our goal in this work is to generate realistic videos given just one initial frame as input. Existing unsupervised approaches to this task do not consider the fact that a video typ…
Computational Design of Cold Bent Glass Façades
Konstantinos Gavriil, Ruslan Guseinov, Jesús Pérez +5
Cold bent glass is a promising and cost-efficient method for realizing doubly curved glass façades. They are produced by attaching planar glass sheets to curved frames and require…
Unsupervised object-centric video generation and decomposition in 3D
Paul Henderson, Christoph H. Lampert
A natural approach to generative modeling of videos is to represent them as a composition of moving objects. Recent works model a set of 2D sprites over a slowly-varying background…
Leveraging 2D Data to Learn Textured 3D Mesh Generation
Paul Henderson, Vagia Tsiminaki, Christoph H. Lampert
Numerous methods have been proposed for probabilistic generative modelling of 3D objects. However, none of these is able to produce textured objects, which renders them of limited…
Object-Centric Image Generation with Factored Depths, Locations, and Appearances
Titas Anciukevicius, Christoph H. Lampert, Paul Henderson
We present a generative model of images that explicitly reasons over the set of objects they show. Our model learns a structured latent representation that separates objects from e…
Learning single-image 3D reconstruction by generative modelling of shape, pose and shading
Paul Henderson, Vittorio Ferrari
We present a unified framework tackling two problems: class-specific 3D reconstruction from a single image, and generation of new 3D shape samples. These tasks have received consid…
Learning to Generate and Reconstruct 3D Meshes with only 2D Supervision
Paul Henderson, Vittorio Ferrari
We present a unified framework tackling two problems: class-specific 3D reconstruction from a single image, and generation of new 3D shape samples. These tasks have received consid…
Automatic Generation of Constrained Furniture Layouts
Paul Henderson, Kartic Subr, Vittorio Ferrari
Efficient authoring of vast virtual environments hinges on algorithms that are able to automatically generate content while also being controllable. We propose a method to automati…
End-to-end training of object class detectors for mean average precision
Paul Henderson, Vittorio Ferrari
We present a method for training CNN-based object class detectors directly using mean average precision (mAP) as the training loss, in a truly end-to-end fashion that includes non-…
Automatically selecting inference algorithms for discrete energy minimisation
Paul Henderson, Vittorio Ferrari
Minimisation of discrete energies defined over factors is an important problem in computer vision, and a vast number of MAP inference algorithms have been proposed. Different infer…