BAM! the Behance Artistic Media Dataset for Recognition beyond Photography
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BAM! the Behance Artistic Media Dataset for Recognition beyond Photography. / Wilber, Michael J.; Fang, Chen; Jin, Hailin; Hertzmann, Aaron; Collomosse, John; Belongie, Serge.
In: Proceedings of the IEEE International Conference on Computer Vision, 22.12.2017, p. 1211-1220.Research output: Contribution to journal › Conference article › Research › peer-review
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TY - GEN
T1 - BAM! the Behance Artistic Media Dataset for Recognition beyond Photography
AU - Wilber, Michael J.
AU - Fang, Chen
AU - Jin, Hailin
AU - Hertzmann, Aaron
AU - Collomosse, John
AU - Belongie, Serge
N1 - Funding Information: This work is partly funded by an NSF Graduate Research Fellowship award (NSF DGE-1144153, Author 1), a Google Focused Research award (Author 6), a Facebook equipment donation to Cornell University, and Adobe Research. Publisher Copyright: © 2017 IEEE.
PY - 2017/12/22
Y1 - 2017/12/22
N2 - Computer vision systems are designed to work well within the context of everyday photography. However, artists often render the world around them in ways that do not resemble photographs. Artwork produced by people is not constrained to mimic the physical world, making it more challenging for machines to recognize.,,This work is a step toward teaching machines how to categorize images in ways that are valuable to humans. First, we collect a large-scale dataset of contemporary artwork from Behance, a website containing millions of portfolios from professional and commercial artists. We annotate Behance imagery with rich attribute labels for content, emotions, and artistic media. Furthermore, we carry out baseline experiments to show the value of this dataset for artistic style prediction, for improving the generality of existing object classifiers, and for the study of visual domain adaptation. We believe our Behance Artistic Media dataset will be a good starting point for researchers wishing to study artistic imagery and relevant problems. This dataset can be found at https://bam-dataset.org/
AB - Computer vision systems are designed to work well within the context of everyday photography. However, artists often render the world around them in ways that do not resemble photographs. Artwork produced by people is not constrained to mimic the physical world, making it more challenging for machines to recognize.,,This work is a step toward teaching machines how to categorize images in ways that are valuable to humans. First, we collect a large-scale dataset of contemporary artwork from Behance, a website containing millions of portfolios from professional and commercial artists. We annotate Behance imagery with rich attribute labels for content, emotions, and artistic media. Furthermore, we carry out baseline experiments to show the value of this dataset for artistic style prediction, for improving the generality of existing object classifiers, and for the study of visual domain adaptation. We believe our Behance Artistic Media dataset will be a good starting point for researchers wishing to study artistic imagery and relevant problems. This dataset can be found at https://bam-dataset.org/
UR - http://www.scopus.com/inward/record.url?scp=85041906992&partnerID=8YFLogxK
U2 - 10.1109/ICCV.2017.136
DO - 10.1109/ICCV.2017.136
M3 - Conference article
AN - SCOPUS:85041906992
SP - 1211
EP - 1220
JO - Proceedings of the IEEE International Conference on Computer Vision
JF - Proceedings of the IEEE International Conference on Computer Vision
SN - 1550-5499
T2 - 16th IEEE International Conference on Computer Vision, ICCV 2017
Y2 - 22 October 2017 through 29 October 2017
ER -
ID: 301826896