{"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/ai-challenger-a-large-scale-dataset-for-going","title":"AI Challenger : A Large-scale Dataset for Going Deeper in Image Understanding","arxiv_id":"1711.06475","date":"2017-11-17","proceeding":null,"authors":["Jiahong Wu","He Zheng","Bo Zhao","Yixin Li","Baoming Yan","Rui Liang","Wenjia Wang","Shipei Zhou","Guosen Lin","Yanwei Fu","Yizhou Wang","Yonggang Wang"],"abstract":"Significant progress has been achieved in Computer Vision by leveraging\nlarge-scale image datasets. However, large-scale datasets for complex Computer\nVision tasks beyond classification are still limited. This paper proposed a\nlarge-scale dataset named AIC (AI Challenger) with three sub-datasets, human\nkeypoint detection (HKD), large-scale attribute dataset (LAD) and image Chinese\ncaptioning (ICC). In this dataset, we annotate class labels (LAD), keypoint\ncoordinate (HKD), bounding box (HKD and LAD), attribute (LAD) and caption\n(ICC). These rich annotations bridge the semantic gap between low-level images\nand high-level concepts. The proposed dataset is an effective benchmark to\nevaluate and improve different computational methods. In addition, for related\ntasks, others can also use our dataset as a new resource to pre-train their\nmodels.","url_abs":"http://arxiv.org/abs/1711.06475v1","url_pdf":"http://arxiv.org/pdf/1711.06475v1.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":"ai-challenger-a-large-scale-dataset-for-going","repo_url":"https://github.com/AIChallenger/AI_Challenger","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"unanswered"}},{"paper_slug":"ai-challenger-a-large-scale-dataset-for-going","repo_url":"https://github.com/chingswy/HumanPoseMemo","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"ai-challenger-a-large-scale-dataset-for-going","repo_url":"https://github.com/open-mmlab/mmpose","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":"attribute","task_name":"Attribute"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"keypoint-detection","task_name":"Keypoint Detection"}],"methods":[],"datasets_introduced":[{"slug":"aic","name":"AIC","full_name":"AI Challenger"}],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1711.06475","atlas_url":"https://app.syntology.ai/?focus=1711.06475","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}