{"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-recognition-of-thousands-of","title":"Fine-Grained Recognition of Thousands of Object Categories With Single-Example Training","arxiv_id":null,"date":"2017-07-01","proceeding":"CVPR 2017 7","authors":["Leonid Karlinsky","Joseph Shtok","Yochay Tzur","Asaf Tzadok"],"abstract":"We approach the problem of fast detection and recognition of a large number (thousands) of object categories while training on a very limited amount of examples, usually one per category. Examples of this task include: (i) detection of retail products, where we have only one studio image of each product available for training; (ii) detection of brand logos; and (iii) detection of 3D objects and their respective poses within a static 2D image, where only a sparse subset of (partial) object views is available for training, with a single example for each view. Building a detector based on so few examples presents a significant challenge for the current top-performing (deep) learning based techniques, which require large amounts of data to train. Our approach for this task is based on a non-parametric probabilistic model for initial detection, CNN-based refinement and temporal integration where applicable. We successfully demonstrate its usefulness in a variety of experiments on both existing and our own benchmarks achieving state-of-the-art performance.\r","url_abs":"http://openaccess.thecvf.com/content_cvpr_2017/html/Karlinsky_Fine-Grained_Recognition_of_CVPR_2017_paper.html","url_pdf":"http://openaccess.thecvf.com/content_cvpr_2017/papers/Karlinsky_Fine-Grained_Recognition_of_CVPR_2017_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":[{"paper_slug":"fine-grained-recognition-of-thousands-of","repo_url":"https://github.com/leokarlin/msmo_star_model","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}