The iMet Collection 2019 Challenge Dataset
Research output: Working paper › Preprint › Research
Standard
The iMet Collection 2019 Challenge Dataset. / Belongie, Serge; Zhang, Chenyang; Kaeser-Chen, Christine; Vesom, Grace; Choi, Jennie; Kessler, Maria.
2019.Research output: Working paper › Preprint › Research
Harvard
APA
Vancouver
Author
Bibtex
}
RIS
TY - UNPB
T1 - The iMet Collection 2019 Challenge Dataset
AU - Belongie, Serge
AU - Zhang, Chenyang
AU - Kaeser-Chen, Christine
AU - Vesom, Grace
AU - Choi, Jennie
AU - Kessler, Maria
PY - 2019/6/3
Y1 - 2019/6/3
N2 - 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.
AB - 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.
UR - https://arxiv.org/abs/1906.00901
M3 - Preprint
BT - The iMet Collection 2019 Challenge Dataset
ER -
ID: 304517460