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

Belongie, S, Zhang, C, Kaeser-Chen, C, Vesom, G, Choi, J & Kessler, M 2019 'The iMet Collection 2019 Challenge Dataset'. <https://vision.cornell.edu/se3/wp-content/uploads/2019/06/iMet2019.pdf>

APA

Belongie, S., Zhang, C., Kaeser-Chen, C., Vesom, G., Choi, J., & Kessler, M. (2019). The iMet Collection 2019 Challenge Dataset. https://vision.cornell.edu/se3/wp-content/uploads/2019/06/iMet2019.pdf

Vancouver

Belongie S, Zhang C, Kaeser-Chen C, Vesom G, Choi J, Kessler M. The iMet Collection 2019 Challenge Dataset. 2019 Jun 3.

Author

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

Bibtex

@techreport{3599c7465b0a435a8911b0aacdaf9f5c,
title = "The iMet Collection 2019 Challenge Dataset",
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.",
author = "Serge Belongie and Chenyang Zhang and Christine Kaeser-Chen and Grace Vesom and Jennie Choi and Maria Kessler",
year = "2019",
month = jun,
day = "3",
language = "English",
type = "WorkingPaper",

}

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