BAM! The Behance Artistic Media Dataset for Recognition Beyond Photography

Michael J. Wilber, Chen Fang, Hailin Jin, Aaron Hertzmann, John Collomosse, Serge Belongie

Abstract:

he 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.