Artist-specific style transfer for semantic segmentation of paintings: The value of large corpora of surrogate artworks

Abstract

Deep neural networks for semantic segmentation have recently outperformed other methods for natural images, partly due to the abundance of training data for this case. However, applying these networks to pictures from a different domain often leads to a significant drop in accuracy. Fine art paintings for highly stylized works, such as from Cubism or Expressionism, in particular, are challenging due to large deviations in shape and texture of certain objects when compared to natural images. In this paper, we demonstrate that style transfer can be used as a form of data augmentation during the training of CNN based semantic segmentation models to improve the accuracy of semantic segmentation models in art pieces of a specific artist. For this, we pick a selection of paintings from a specific style for the painters Egon Schiele, Vincent Van Gogh, Pablo Picasso and Willem de Kooning, create stylized training dataset by transferring artist-specific style to natural photographs and show that training the same segmentation network on a surrogate artworks improves the accuracy for fine art paintings. We also provide a dataset with pixel-level annotation of 60 fine art paintings to the public and for evaluation of our method.