{"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/fine-grained-visual-comparisons-with-local","title":"Fine-Grained Visual Comparisons with Local Learning","arxiv_id":null,"date":"2014-06-01","proceeding":"CVPR 2014 6","authors":["Aron Yu","Kristen Grauman"],"abstract":"Given two images, we want to predict which exhibits a particular visual attribute more than the other---even when the two images are quite similar.  Existing relative attribute methods rely on global ranking functions; yet rarely will the visual cues relevant to a comparison be constant for all data, nor will humans' perception of the attribute necessarily permit a global ordering.  To address these issues, we propose a local learning approach for fine-grained visual comparisons.  Given a novel pair of images, we learn a local ranking model on the fly, using only analogous training comparisons.  We show how to identify these analogous pairs using learned metrics.  With results on three challenging datasets -- including a large newly curated dataset for fine-grained comparisons -- our method outperforms state-of-the-art methods for relative attribute prediction.","url_abs":"http://openaccess.thecvf.com/content_cvpr_2014/html/Yu_Fine-Grained_Visual_Comparisons_2014_CVPR_paper.html","url_pdf":"http://openaccess.thecvf.com/content_cvpr_2014/papers/Yu_Fine-Grained_Visual_Comparisons_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":"attribute","task_name":"Attribute"}],"methods":[],"datasets_introduced":[{"slug":"ut-zappos50k","name":"UT Zappos50K","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}