{"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/birdsnap-large-scale-fine-grained-visual","title":"Birdsnap: Large-scale Fine-grained Visual Categorization of Birds","arxiv_id":null,"date":"2014-06-01","proceeding":"CVPR 2014 6","authors":["Thomas Berg","Jiongxin Liu","Seung Woo Lee","Michelle L. Alexander","David W. Jacobs","Peter N. Belhumeur"],"abstract":"We address the problem of large-scale fine-grained visual categorization, describing new methods we have used to produce an online field guide to 500 North American bird species.  We focus on the challenges raised when such a system is asked to distinguish between highly similar species of birds.  First, we introduce   \"one-vs-most classifiers.\"  By eliminating highly similar species during training, these classifiers achieve more accurate and intuitive results than common one-vs-all classifiers.  Second, we show how to estimate spatio-temporal class priors from observations that are sampled at irregular and biased locations.  We show how these priors can be used to significantly improve performance.  We then show state-of-the-art recognition performance on a new, large dataset that we make publicly available.  These recognition methods are integrated into the online field guide, which is also publicly available. ","url_abs":"http://openaccess.thecvf.com/content_cvpr_2014/html/Berg_Birdsnap_Large-scale_Fine-grained_2014_CVPR_paper.html","url_pdf":"http://openaccess.thecvf.com/content_cvpr_2014/papers/Berg_Birdsnap_Large-scale_Fine-grained_2014_CVPR_paper.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":"fine-grained-visual-categorization","task_name":"Fine-Grained Visual Categorization"}],"methods":[],"datasets_introduced":[{"slug":"birdsnap","name":"Birdsnap","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}