{"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/parsing-r-cnn-for-instance-level-human","title":"Parsing R-CNN for Instance-Level Human Analysis","arxiv_id":"1811.12596","date":"2018-11-30","proceeding":"CVPR 2019 6","authors":["Lu Yang","Qing Song","Zhihui Wang","Ming Jiang"],"abstract":"Instance-level human analysis is common in real-life scenarios and has\nmultiple manifestations, such as human part segmentation, dense pose\nestimation, human-object interactions, etc. Models need to distinguish\ndifferent human instances in the image panel and learn rich features to\nrepresent the details of each instance. In this paper, we present an end-to-end\npipeline for solving the instance-level human analysis, named Parsing R-CNN. It\nprocesses a set of human instances simultaneously through comprehensive\nconsidering the characteristics of region-based approach and the appearance of\na human, thus allowing representing the details of instances. Parsing R-CNN is\nvery flexible and efficient, which is applicable to many issues in human\ninstance analysis. Our approach outperforms all state-of-the-art methods on\nCIHP (Crowd Instance-level Human Parsing), MHP v2.0 (Multi-Human Parsing) and\nDensePose-COCO datasets. Based on the proposed Parsing R-CNN, we reach the 1st\nplace in the COCO 2018 Challenge DensePose Estimation task. Code and models are\npublic available.","url_abs":"http://arxiv.org/abs/1811.12596v1","url_pdf":"http://arxiv.org/pdf/1811.12596v1.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":"parsing-r-cnn-for-instance-level-human","repo_url":"https://github.com/soeaver/Parsing-R-CNN","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"parsing-r-cnn-for-instance-level-human","repo_url":"https://github.com/soeaver/RP-R-CNN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"human-parsing","task_name":"Human Parsing"},{"task_slug":"human-part-segmentation","task_name":"Human Part Segmentation"},{"task_slug":"multi-human-parsing","task_name":"Multi-Human Parsing"},{"task_slug":"pose-estimation","task_name":"Pose Estimation"}],"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":"convolution","method_name":"Convolution"},{"method_slug":"global-average-pooling","method_name":"Global Average Pooling"},{"method_slug":"grouped-convolution","method_name":"Grouped Convolution"},{"method_slug":"kaiming-initialization","method_name":"Kaiming Initialization"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"resnext","method_name":"ResNeXt"},{"method_slug":"resnext-block","method_name":"ResNeXt Block"},{"method_slug":"residual-connection","method_name":"Residual Connection"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/human-part-segmentation-on-cihp","task":"Human Part Segmentation","dataset":"CIHP","model":"Parsing R-CNN + ResNext101","rank_in_archive_order":5,"of":6,"metrics":{"Mean IoU":"61.1"},"uses_additional_data":false},{"leaderboard":"/sota/human-part-segmentation-on-mhp-v20","task":"Human Part Segmentation","dataset":"MHP v2.0","model":"Parsing R-CNN + ResNext101","rank_in_archive_order":1,"of":1,"metrics":{"Mean IoU":"41.8"},"uses_additional_data":false},{"leaderboard":"/sota/pose-estimation-on-densepose-coco","task":"Pose Estimation","dataset":"DensePose-COCO","model":"Parsing R-CNN + ResNext101","rank_in_archive_order":2,"of":4,"metrics":{"AP":"61.6"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1811.12596","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1811.12596"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+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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