{"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/devil-in-the-details-towards-accurate-single","title":"Devil in the Details: Towards Accurate Single and Multiple Human Parsing","arxiv_id":"1809.05996","date":"2018-09-17","proceeding":null,"authors":["Tao Ruan","Ting Liu","Zilong Huang","Yunchao Wei","Shikui Wei","Yao Zhao","Thomas Huang"],"abstract":"Human parsing has received considerable interest due to its wide application\npotentials. Nevertheless, it is still unclear how to develop an accurate human\nparsing system in an efficient and elegant way. In this paper, we identify\nseveral useful properties, including feature resolution, global context\ninformation and edge details, and perform rigorous analyses to reveal how to\nleverage them to benefit the human parsing task. The advantages of these useful\nproperties finally result in a simple yet effective Context Embedding with Edge\nPerceiving (CE2P) framework for single human parsing. Our CE2P is end-to-end\ntrainable and can be easily adopted for conducting multiple human parsing.\nBenefiting the superiority of CE2P, we achieved the 1st places on all three\nhuman parsing benchmarks. Without any bells and whistles, we achieved 56.50\\%\n(mIoU), 45.31\\% (mean $AP^r$) and 33.34\\% ($AP^p_{0.5}$) in LIP, CIHP and MHP\nv2.0, which outperform the state-of-the-arts more than 2.06\\%, 3.81\\% and\n1.87\\%, respectively. We hope our CE2P will serve as a solid baseline and help\nease future research in single/multiple human parsing. Code has been made\navailable at \\url{https://github.com/liutinglt/CE2P}.","url_abs":"http://arxiv.org/abs/1809.05996v3","url_pdf":"http://arxiv.org/pdf/1809.05996v3.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":"devil-in-the-details-towards-accurate-single","repo_url":"https://github.com/liutinglt/CE2P","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"devil-in-the-details-towards-accurate-single","repo_url":"https://github.com/Liuyixuan95/DORN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"exists_but_no_tree"}}],"tasks":[{"task_slug":"human-parsing","task_name":"Human Parsing"},{"task_slug":"person-re-identification","task_name":"Person Re-Identification"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"}],"methods":[{"method_slug":"1x1-convolution","method_name":"1x1 Convolution"},{"method_slug":"average-pooling","method_name":"Average Pooling"},{"method_slug":"batch-normalization","method_name":"Batch Normalization"},{"method_slug":"bottleneck-residual-block","method_name":"Bottleneck Residual Block"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"global-average-pooling","method_name":"Global Average Pooling"},{"method_slug":"kaiming-initialization","method_name":"Kaiming Initialization"},{"method_slug":"max-pooling","method_name":"Max Pooling"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"residual-block","method_name":"Residual Block"},{"method_slug":"residual-connection","method_name":"Residual Connection"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/person-re-identification-on-market-1501-c","task":"Person Re-Identification","dataset":"Market-1501-C","model":"CaceNet","rank_in_archive_order":2,"of":22,"metrics":{" Rank-1":"42.92"," mAP":"18.24"," mINP":"0.67"},"uses_additional_data":false},{"leaderboard":"/sota/semantic-segmentation-on-lip-val","task":"Semantic Segmentation","dataset":"LIP val","model":"CE2P (ResNet-101)","rank_in_archive_order":9,"of":13,"metrics":{"mIoU":"53.10%"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/1809.05996","atlas_url":"https://app.syntology.ai/?focus=1809.05996","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1809.05996"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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