The iMet Collection 2019 Challenge Dataset

Chenyang Zhang; Christine Kaeser-Chen; Grace Vesom; Jennie Choi; Maria Kessler; Serge Belongie

The iMet Collection 2019 Challenge Dataset

Computer Vision and Pattern Recognition 2019-06-05 v2

Authors: Chenyang Zhang , Christine Kaeser-Chen , Grace Vesom , Jennie Choi , Maria Kessler , Serge Belongie

Abstract

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.

Keywords

fine-grained visual classification image classification image processing

Cite

@article{arxiv.1906.00901,
  title  = {The iMet Collection 2019 Challenge Dataset},
  author = {Chenyang Zhang and Christine Kaeser-Chen and Grace Vesom and Jennie Choi and Maria Kessler and Serge Belongie},
  journal= {arXiv preprint arXiv:1906.00901},
  year   = {2019}
}

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3 pages, 4 figures

The iMet Collection 2019 Challenge Dataset

Abstract

Keywords

Cite

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