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Computer vision systems are designed to work well within the context ofeveryday photography. However, artists often render the world around them inways that do not resemble photographs. Artwork produced by people is notconstrained to mimic the physical world, making it more challenging formachines to recognize.  This work is a step toward teaching machines how to categorize images in waysthat are valuable to humans. First, we collect a large-scale dataset ofcontemporary artwork from Behance, a website containing millions of portfoliosfrom professional and commercial artists. We annotate Behance imagery with richattribute labels for content, emotions, and artistic media. Furthermore, wecarry out baseline experiments to show the value of this dataset for artisticstyle prediction, for improving the generality of existing object classifiers,and for the study of visual domain adaptation. We believe our Behance ArtisticMedia dataset will be a good starting point for researchers wishing to studyartistic imagery and relevant problems.

BAM! The Behance Artistic Media Dataset for Recognition Beyond Photography