BAM! the Behance Artistic Media Dataset for Recognition beyond Photography
Research output: Contribution to journal › Conference article › Research › peer-review
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/
Original language | English |
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Journal | Proceedings of the IEEE International Conference on Computer Vision |
Pages (from-to) | 1211-1220 |
Number of pages | 10 |
ISSN | 1550-5499 |
DOIs | |
Publication status | Published - 22 Dec 2017 |
Externally published | Yes |
Event | 16th IEEE International Conference on Computer Vision, ICCV 2017 - Venice, Italy Duration: 22 Oct 2017 → 29 Oct 2017 |
Conference
Conference | 16th IEEE International Conference on Computer Vision, ICCV 2017 |
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Country | Italy |
City | Venice |
Period | 22/10/2017 → 29/10/2017 |
Bibliographical note
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.
ID: 301826896