1 code implementation • ECCV 2020 • Dipu Manandhar, Dan Ruta, John Collomosse
We propose a novel representation learning technique for measuring the similarity of user interface designs.
no code implementations • 11 Mar 2023 • Gemma Canet Tarrés, Dan Ruta, Tu Bui, John Collomosse
We propose PARASOL, a multi-modal synthesis model that enables disentangled, parametric control of the visual style of the image by jointly conditioning synthesis on both content and a fine-grained visual style embedding.
no code implementations • 15 Feb 2023 • Simon Jenni, Alexander Black, John Collomosse
We propose a self-supervised learning approach for videos that learns representations of both the RGB frames and the accompanying audio without human supervision.
no code implementations • 21 Nov 2022 • Yu Zeng, Zhe Lin, Jianming Zhang, Qing Liu, John Collomosse, Jason Kuen, Vishal M. Patel
We propose a new framework for conditional image synthesis from semantic layouts of any precision levels, ranging from pure text to a 2D semantic canvas with precise shapes.
no code implementations • 17 Aug 2022 • Xin Yuan, Zhe Lin, Jason Kuen, Jianming Zhang, John Collomosse
We develop an approach for text-to-image generation that embraces additional retrieval images, driven by a combination of implicit visual guidance loss and generative objectives.
no code implementations • 9 Aug 2022 • Dan Ruta, Andrew Gilbert, Saeid Motiian, Baldo Faieta, Zhe Lin, John Collomosse
We present HyperNST; a neural style transfer (NST) technique for the artistic stylization of images, based on Hyper-networks and the StyleGAN2 architecture.
1 code implementation • 26 Jul 2022 • Trisha Mittal, Ritwik Sinha, Viswanathan Swaminathan, John Collomosse, Dinesh Manocha
To this end, we present VideoSham, a dataset consisting of 826 videos (413 real and 413 manipulated).
1 code implementation • 5 Jul 2022 • Tu Bui, Ning Yu, John Collomosse
Uniquely, we present a solution to this task capable of 1) matching images invariant to their semantic content; 2) robust to benign transformations (changes in quality, resolution, shape, etc.)
no code implementations • 28 Jun 2022 • Alexander Black, Tu Bui, Simon Jenni, Zhifei Zhang, Viswanathan Swaminanthan, John Collomosse
We present SImProv - a scalable image provenance framework to match a query image back to a trusted database of originals and identify possible manipulations on the query.
no code implementations • 17 Mar 2022 • Cusuh Ham, Gemma Canet Tarres, Tu Bui, James Hays, Zhe Lin, John Collomosse
CoGS enables exploration of diverse appearance possibilities for a given sketched object, enabling decoupled control over the structure and the appearance of the output.
no code implementations • 10 Mar 2022 • Dan Ruta, Andrew Gilbert, Pranav Aggarwal, Naveen Marri, Ajinkya Kale, Jo Briggs, Chris Speed, Hailin Jin, Baldo Faieta, Alex Filipkowski, Zhe Lin, John Collomosse
We present StyleBabel, a unique open access dataset of natural language captions and free-form tags describing the artistic style of over 135K digital artworks, collected via a novel participatory method from experts studying at specialist art and design schools.
no code implementations • 25 Feb 2022 • Maksym Andriushchenko, Xiaoyang Rebecca Li, Geoffrey Oxholm, Thomas Gittings, Tu Bui, Nicolas Flammarion, John Collomosse
Finally, we show how to train an adversarially robust image comparator model for detecting editorial changes in matched images.
no code implementations • 21 Sep 2021 • Alexander Black, Tu Bui, Simon Jenni, Vishy Swaminathan, John Collomosse
We present VPN - a content attribution method for recovering provenance information from videos shared online.
1 code implementation • 16 Aug 2021 • Leo Sampaio Ferraz Ribeiro, Tu Bui, John Collomosse, Moacir Ponti
Scene Designer is a novel method for searching and generating images using free-hand sketches of scene compositions; i. e. drawings that describe both the appearance and relative positions of objects.
no code implementations • ICCV 2021 • Eric Nguyen, Tu Bui, Vishy Swaminathan, John Collomosse
Our key contribution is OSCAR-Net (Object-centric Scene Graph Attention for Image Attribution Network); a robust image hashing model inspired by recent successes of Transformers in the visual domain.
1 code implementation • 15 Jun 2021 • Alexander Black, Tu Bui, Long Mai, Hailin Jin, John Collomosse
We present an algorithm for searching image collections using free-hand sketches that describe the appearance and relative positions of multiple objects.
no code implementations • CVPR 2021 • Dipu Manandhar, Hailin Jin, John Collomosse
We present Magic Layouts; a method for parsing screenshots or hand-drawn sketches of user interface (UI) layouts.
no code implementations • ICCV 2021 • Dan Ruta, Saeid Motiian, Baldo Faieta, Zhe Lin, Hailin Jin, Alex Filipkowski, Andrew Gilbert, John Collomosse
We present ALADIN (All Layer AdaIN); a novel architecture for searching images based on the similarity of their artistic style.
1 code implementation • NeurIPS 2020 • Tong He, John Collomosse, Hailin Jin, Stefano Soatto
We propose Geo-PIFu, a method to recover a 3D mesh from a monocular color image of a clothed person.
1 code implementation • CVPR 2020 • Leo Sampaio Ferraz Ribeiro, Tu Bui, John Collomosse, Moacir Ponti
Sketchformer is a novel transformer-based representation for encoding free-hand sketches input in a vector form, i. e. as a sequence of strokes.
2 code implementations • 14 Jan 2020 • Kary Ho, Andrew Gilbert, Hailin Jin, John Collomosse
We present a neural architecture search (NAS) technique to enhance the performance of unsupervised image de-noising, in-painting and super-resolution under the recently proposed Deep Image Prior (DIP).
1 code implementation • ICCV 2019 • Haotian Zhang, Long Mai, Ning Xu, Zhaowen Wang, John Collomosse, Hailin Jin
We propose a novel video inpainting algorithm that simultaneously hallucinates missing appearance and motion (optical flow) information, building upon the recent 'Deep Image Prior' (DIP) that exploits convolutional network architectures to enforce plausible texture in static images.
no code implementations • 8 Aug 2019 • Andrew Gilbert, Matthew Trumble, Adrian Hilton, John Collomosse
We aim to simultaneously estimate the 3D articulated pose and high fidelity volumetric occupancy of human performance, from multiple viewpoint video (MVV) with as few as two views.
Ranked #129 on 3D Human Pose Estimation on Human3.6M
no code implementations • 3 Jul 2019 • Thomas Gittings, Steve Schneider, John Collomosse
We present a novel method for generating robust adversarial image examples building upon the recent `deep image prior' (DIP) that exploits convolutional network architectures to enforce plausible texture in image synthesis.
no code implementations • 15 May 2019 • Yifan Yang, Daniel Cooper, John Collomosse, Constantin C. Drăgan, Mark Manulis, Jamie Steane, Arthi Manohar, Jo Briggs, Helen Jones, Wendy Moncur
We present a novel blockchain based service for proving the provenance of online digital identity, exposed as an assistive tool to help non-expert users make better decisions about whom to trust online.
no code implementations • 26 Apr 2019 • Tu Bui, Daniel Cooper, John Collomosse, Mark Bell, Alex Green, John Sheridan, Jez Higgins, Arindra Das, Jared Keller, Olivier Thereaux, Alan Brown
We present ARCHANGEL; a novel distributed ledger based system for assuring the long-term integrity of digital video archives.
no code implementations • CVPR 2019 • John Collomosse, Tu Bui, Hailin Jin
LiveSketch is a novel algorithm for searching large image collections using hand-sketched queries.
no code implementations • ECCV 2018 • Andrew Gilbert, Marco Volino, John Collomosse, Adrian Hilton
We present a convolutional autoencoder that enables high fidelity volumetric reconstructions of human performance to be captured from multi-view video comprising only a small set of camera views.
no code implementations • ECCV 2018 • Matthew Trumble, Andrew Gilbert, Adrian Hilton, John Collomosse
We present a method for simultaneously estimating 3D human pose and body shape from a sparse set of wide-baseline camera views.
Ranked #9 on 3D Human Pose Estimation on Total Capture
no code implementations • CVPR 2018 • Andrew Gilbert, John Collomosse, Hailin Jin, Brian Price
Content-aware image completion or in-painting is a fundamental tool for the correction of defects or removal of objects in images.
no code implementations • ICCV 2017 • John Collomosse, Tu Bui, Michael J. Wilber, Chen Fang, Hailin Jin
We propose a novel measure of visual similarity for image retrieval that incorporates both structural and aesthetic (style) constraints.
no code implementations • ICCV 2017 • Michael J. Wilber, Chen Fang, Hailin Jin, Aaron Hertzmann, John Collomosse, Serge Belongie
Furthermore, we carry out baseline experiments to show the value of this dataset for artistic style prediction, for improving the generality of existing object classifiers, and for the study of visual domain adaptation.
no code implementations • 16 Nov 2016 • Tu Bui, Leonardo Ribeiro, Moacir Ponti, John Collomosse
We propose and evaluate several triplet CNN architectures for measuring the similarity between sketches and photographs, within the context of the sketch based image retrieval (SBIR) task.