{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/bam-the-behance-artistic-media-dataset-for","title":"BAM! The Behance Artistic Media Dataset for Recognition Beyond Photography","arxiv_id":"1704.08614","date":"2017-04-27","proceeding":"ICCV 2017 10","authors":["Michael J. Wilber","Chen Fang","Hailin Jin","Aaron Hertzmann","John Collomosse","Serge Belongie"],"abstract":"Computer vision systems are designed to work well within the context of\neveryday photography. However, artists often render the world around them in\nways that do not resemble photographs. Artwork produced by people is not\nconstrained to mimic the physical world, making it more challenging for\nmachines to recognize.\n  This work is a step toward teaching machines how to categorize images in ways\nthat are valuable to humans. First, we collect a large-scale dataset of\ncontemporary artwork from Behance, a website containing millions of portfolios\nfrom professional and commercial artists. We annotate Behance imagery with rich\nattribute labels for content, emotions, and artistic media. Furthermore, we\ncarry out baseline experiments to show the value of this dataset for artistic\nstyle prediction, for improving the generality of existing object classifiers,\nand for the study of visual domain adaptation. We believe our Behance Artistic\nMedia dataset will be a good starting point for researchers wishing to study\nartistic imagery and relevant problems.","url_abs":"http://arxiv.org/abs/1704.08614v2","url_pdf":"http://arxiv.org/pdf/1704.08614v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[],"tasks":[{"task_slug":"attribute","task_name":"Attribute"},{"task_slug":"domain-adaptation","task_name":"Domain Adaptation"}],"methods":[],"datasets_introduced":[{"slug":"bam","name":"BAM!","full_name":"Behance Artistic Media"}],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1704.08614","atlas_url":"https://app.syntology.ai/?focus=1704.08614","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}