1 citations · 1 across the 4 of their papers we have counts for
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Extracting associations and meanings of objects depicted in artworks through bi-modal deep networks
Gregory Kell, Ryan-Rhys Griffiths, Anthony Bourached +1
We present a novel bi-modal system based on deep networks to address the problem of learning associations and simple meanings of objects depicted in "authored" images, such as fine…
Computational identification of significant actors in paintings through symbols and attributes
David G. Stork, Anthony Bourached, George H. Cann +1
The automatic analysis of fine art paintings presents a number of novel technical challenges to artificial intelligence, computer vision, machine learning, and knowledge representa…
Resolution enhancement in the recovery of underdrawings via style transfer by generative adversarial deep neural networks
George Cann, Anthony Bourached, Ryan-Rhys Griffiths +1
We apply generative adversarial convolutional neural networks to the problem of style transfer to underdrawings and ghost-images in x-rays of fine art paintings with a special focu…
Recovery of underdrawings and ghost-paintings via style transfer by deep convolutional neural networks: A digital tool for art scholars
Anthony Bourached, George Cann, Ryan-Rhys Griffiths +1
We describe the application of convolutional neural network style transfer to the problem of improved visualization of underdrawings and ghost-paintings in fine art oil paintings.…
Generative Model-Enhanced Human Motion Prediction
Anthony Bourached, Ryan-Rhys Griffiths, Robert Gray +2
The task of predicting human motion is complicated by the natural heterogeneity and compositionality of actions, necessitating robustness to distributional shifts as far as out-of-…
Unsupervised Videographic Analysis of Rodent Behaviour
Anthony Bourached, Parashkev Nachev
Animal behaviour is complex and the amount of data in the form of video, if extracted, is copious. Manual analysis of behaviour is massively limited by two insurmountable obstacles…