{"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/detector-in-detector-multi-level-analysis-for","title":"Detector-in-Detector: Multi-Level Analysis for Human-Parts","arxiv_id":"1902.07017","date":"2019-02-19","proceeding":null,"authors":["Xiaojie Li","Lu Yang","Qing Song","Fuqiang Zhou"],"abstract":"Vision-based person, hand or face detection approaches have achieved\nincredible success in recent years with the development of deep convolutional\nneural network (CNN). In this paper, we take the inherent correlation between\nthe body and body parts into account and propose a new framework to boost up\nthe detection performance of the multi-level objects. In particular, we adopt a\nregion-based object detection structure with two carefully designed detectors\nto separately pay attention to the human body and body parts in a\ncoarse-to-fine manner, which we call Detector-in-Detector network (DID-Net).\nThe first detector is designed to detect human body, hand, and face. The second\ndetector, based on the body detection results of the first detector, mainly\nfocus on the detection of small hand and face inside each body. The framework\nis trained in an end-to-end way by optimizing a multi-task loss. Due to the\nlack of human body, face and hand detection dataset, we have collected and\nlabeled a new large dataset named Human-Parts with 14,962 images and 106,879\nannotations. Experiments show that our method can achieve excellent performance\non Human-Parts.","url_abs":"http://arxiv.org/abs/1902.07017v1","url_pdf":"http://arxiv.org/pdf/1902.07017v1.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":"detector-in-detector-multi-level-analysis-for","repo_url":"https://github.com/xiaojie1017/Human-Parts","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"detector-in-detector-multi-level-analysis-for","repo_url":"https://github.com/svjack/Detector-in-Detector","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"body-detection","task_name":"Body Detection"},{"task_slug":"face-detection","task_name":"Face Detection"},{"task_slug":"hand-detection","task_name":"Hand Detection"},{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"object-detection-1","task_name":"object-detection"}],"methods":[],"datasets_introduced":[{"slug":"human-parts","name":"Human-Parts","full_name":null}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}