  Computer vision systems are designed to work well within the context of
everyday photography. However, artists often render the world around them in
ways that do not resemble photographs. Artwork produced by people is not
constrained to mimic the physical world, making it more challenging for
machines to recognize.
  This work is a step toward teaching machines how to categorize images in ways
that are valuable to humans. First, we collect a large-scale dataset of
contemporary artwork from Behance, a website containing millions of portfolios
from professional and commercial artists. We annotate Behance imagery with rich
attribute labels for content, emotions, and artistic media. 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. We believe our Behance Artistic
Media dataset will be a good starting point for researchers wishing to study
artistic imagery and relevant problems.
