  In this work we propose a new deep multibranch neural network to solve the
tasks of artist, style, and genre categorization in a multitask formulation. In
order to gather clues from low-level texture details and, at the same time,
exploit the coarse layout of the painting, the branches of the proposed
networks are fed with crops at different resolutions. We propose and compare
two different crop strategies: the first one is a random-crop strategy that
permits to manage the tradeoff between accuracy and speed; the second one is a
smart extractor based on Spatial Transformer Networks trained to extract the
most representative subregions. Furthermore, inspired by the results obtained
in other domains, we experiment the joint use of hand-crafted features directly
computed on the input images along with neural ones. Experiments are performed
on a new dataset originally sourced from wikiart.org and hosted by Kaggle, and
made suitable for artist, style and genre multitask learning. The dataset here
proposed, named MultitaskPainting100k, is composed by 100K paintings, 1508
artists, 125 styles and 41 genres. Our best method, tested on the
MultitaskPainting100k dataset, achieves accuracy levels of 56.5%, 57.2%, and
63.6% on the tasks of artist, style and genre prediction respectively.
