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Found 4 papers, 2 papers with code
Deep transfer learning for visual analysis and attribution of paintings by Raphael
Visual analysis and authentication of artworks are challenging tasks central to art history and criticism.
Extracting associations and meanings of objects depicted in artworks through bi-modal deep networks
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 art paintings and drawings.
Computational identification of significant actors in paintings through symbols and attributes
The automatic analysis of fine art paintings presents a number of novel technical challenges to artificial intelligence, computer vision, machine learning, and knowledge representation quite distinct from those arising in the analysis of traditional photographs.
Recovery of underdrawings and ghost-paintings via style transfer by deep convolutional neural networks: A digital tool for art scholars
Past methods for inferring color in underdrawings have been based on physical x-ray fluorescence spectral imaging of pigments in ghost-paintings and are thus expensive, time consuming, and require equipment not available in most conservation studios.
