The iMet Collection 2019 Challenge Dataset
This work addresses the problem of fine-grained visual recognition for digital curation in museums, but it is incremental as it builds on existing datasets and methods.
The authors introduced a new dataset for fine-grained artwork attribute recognition, using professional photographs of classic artworks from the Metropolitan Museum of Art with expert-curated annotations, and launched a challenge to advance research in this area.
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.