{"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/benchmark-platform-for-ultra-fine-grained","title":"Benchmark Platform for Ultra-Fine-Grained Visual Categorization Beyond Human Performance","arxiv_id":null,"date":"2021-01-01","proceeding":"ICCV 2021 10","authors":["Xiaohan Yu","Yang Zhao","Yongsheng Gao","Xiaohui Yuan","Shengwu Xiong"],"abstract":"    Deep learning methods have achieved remarkable success in fine-grained visual categorization. Such successful categorization at sub-ordinate level, e.g., different animal or plant species, however relies heavily on the visual differences that human can observe and the ground-truths are labelled on the basis of such human visual observation. In contrast, few research has been done for visual categorization at the ultra-fine-grained level, i.e., a granularity where even human experts can hardly identify the visual differences or are not yet able to give affirmative labels by inferring observed pattern differences. This paper reports our efforts towards mitigating this research gap. We introduce the ultra-fine-grained (UFG) image dataset, a large collection of 47,114 images from 3,526 categories. All the images in the proposed UFG image dataset are grouped into categories with different confirmed cultivar names. In addition, we perform an extensive evaluation of state-of-the-art fine-grained classification methods on the proposed UFG image dataset as comparative baselines. The proposed UFG image dataset and evaluation protocols is intended to serve as a benchmark platform that can advance research of visual classification from approaching human performance to beyond human ability, via facilitating benchmark data of artificial intelligence (AI) not to be limited by the labels of human intelligence (HI). The dataset is available online at https://github.com/XiaohanYu-GU/Ultra-FGVC.    ","url_abs":"http://openaccess.thecvf.com//content/ICCV2021/html/Yu_Benchmark_Platform_for_Ultra-Fine-Grained_Visual_Categorization_Beyond_Human_Performance_ICCV_2021_paper.html","url_pdf":"http://openaccess.thecvf.com//content/ICCV2021/papers/Yu_Benchmark_Platform_for_Ultra-Fine-Grained_Visual_Categorization_Beyond_Human_Performance_ICCV_2021_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":"benchmark-platform-for-ultra-fine-grained","repo_url":"https://github.com/xiaohanyu-gu/ultra-fgvc","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"fine-grained-visual-categorization","task_name":"Fine-Grained Visual Categorization"}],"methods":[],"datasets_introduced":[{"slug":"ultra-fine-grained-leaves-cotton-soyageing","name":"Ultra Fine-Grained Leaves (Cotton, SoyAgeing, SoyGene, SoyGlobal, SoyLocal)","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}