The iMet Collection 2019 Challenge Dataset

“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.”

The museum as a fine-grained visual categorisation dataset …

Read The iMet Collection 2019 Challenge Dataset by Chenyang Zhang, Christine Kaeser-Chen, Grace Vesom, Jennie Choi, Maria Kessler, Serge Belongie

Google Images view on Curating Social Media vs Curating Contemporary Art

Searching for “Curating Social Media” and “Curating Contemporary Art” leads to two different sets of images. Hard to find any intersection. Strategy, schemas, symbols, pie charts and diagrams versus white cube installation views documented with photographs (scrolling further down leads to famous curators and books).