  Existing computer vision technologies in artwork recognition focus mainly on
instance retrieval or coarse-grained attribute classification. In this work, we
present a novel dataset for fine-grained artwork attribute recognition. The
images in the dataset are professional photographs of classic artworks from the
Metropolitan Museum of Art, and annotations are curated and verified by
world-class museum experts. In addition, we also present the iMet Collection
2019 Challenge as part of the FGVC6 workshop. Through the competition, we aim
to spur the enthusiasm of the fine-grained visual recognition research
community and advance the state-of-the-art in digital curation of museum
collections.
