{"url":"/task/multi-human-parsing","name":"Multi-Human Parsing","slug":"multi-human-parsing","description_markdown":"Multi-human parsing is the task of parsing multiple humans in crowded scenes.\r\n\r\n<span style=\"color:grey; opacity: 0.6\">( Image credit: [Multi-Human Parsing](https://github.com/ZhaoJ9014/Multi-Human-Parsing) )</span>","categories":[{"name":"Computer Vision","url":"/area/computer-vision"}],"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","slug_source":"archive_url"},"counts":{"papers_tagged":11,"papers_with_code":10,"benchmarks":3,"benchmark_tables_in_archive":3,"benchmark_tables_shown":3,"benchmark_tables_withheld_as_spam":0,"benchmark_definition":"a leaderboard table with at least one row; benchmark_tables_shown also counts the zero-row tables; benchmark_tables_in_archive adds the tables withheld as spam","datasets":4,"subtasks":0,"parent_tasks":1},"benchmarks":[{"leaderboard":"/sota/multi-human-parsing-on-mhp-v20","slug":"multi-human-parsing-on-mhp-v20","dataset":"MHP v2.0","dataset_url":"/dataset/mhp","rows_in_archive":5,"metrics":["AP 0.5"],"first_row_in_archive_order":{"model":"UniParser","paper_title":"UniParser: Multi-Human Parsing with Unified Correlation Representation Learning","paper_url":"/paper/uniparser-multi-human-parsing-with-unified","paper_date":"2023-10-13","arxiv_id":"2310.08984","code_links":[{"title":"cjm-sfw/Uniparser","url":"https://github.com/cjm-sfw/Uniparser"}],"syntology":null}},{"leaderboard":"/sota/multi-human-parsing-on-mhp-v10","slug":"multi-human-parsing-on-mhp-v10","dataset":"MHP v1.0","dataset_url":"/dataset/mhp","rows_in_archive":4,"metrics":["AP 0.5"],"first_row_in_archive_order":{"model":"NAN","paper_title":"Understanding Humans in Crowded Scenes: Deep Nested Adversarial Learning and A New Benchmark for Multi-Human Parsing","paper_url":"/paper/understanding-humans-in-crowded-scenes-deep","paper_date":"2018-04-10","arxiv_id":"1804.03287","code_links":[{"title":"open-mmlab/mmpose","url":"https://github.com/open-mmlab/mmpose"},{"title":"ZhaoJ9014/Multi-Human-Parsing","url":"https://github.com/ZhaoJ9014/Multi-Human-Parsing"}],"syntology":null}},{"leaderboard":"/sota/multi-human-parsing-on-pascal-person-part","slug":"multi-human-parsing-on-pascal-person-part","dataset":"PASCAL-Part","dataset_url":"/dataset/pascal-person-part","rows_in_archive":3,"metrics":["AP 0.5"],"first_row_in_archive_order":{"model":"NAN","paper_title":"Understanding Humans in Crowded Scenes: Deep Nested Adversarial Learning and A New Benchmark for Multi-Human Parsing","paper_url":"/paper/understanding-humans-in-crowded-scenes-deep","paper_date":"2018-04-10","arxiv_id":"1804.03287","code_links":[{"title":"open-mmlab/mmpose","url":"https://github.com/open-mmlab/mmpose"},{"title":"ZhaoJ9014/Multi-Human-Parsing","url":"https://github.com/ZhaoJ9014/Multi-Human-Parsing"}],"syntology":null}}],"datasets":[{"url":"/dataset/pascal-person-part","name":"PASCAL-Part","full_name":"PASCAL-Part","num_papers_in_archive":65},{"url":"/dataset/mhp","name":"MHP","full_name":"Multiple-Human Parsing","num_papers_in_archive":14},{"url":"/dataset/ccihp","name":"CCIHP","full_name":"Characterized Crowd Instance-level Human Parsing","num_papers_in_archive":1},{"url":"/dataset/llmafia","name":"LLMafia","full_name":"","num_papers_in_archive":1}],"subtasks":[],"parent_tasks":[{"url":"/task/human-parsing","name":"Human Parsing"}],"papers":{"order":"repositories listed in the archive (desc), then date (desc); the archive holds no stars","population":"papers tagged with this task that list at least one repository in the archive","shown":10,"of":10,"tagged_in_all":11,"items":[{"url":"/paper/mask-r-cnn","title":"Mask R-CNN","date":"2017-03-20","arxiv_id":"1703.06870","repositories_listed":179,"syntology":{"n":140,"n_ran":42,"n_unverified":98,"n_pointer_only":23}},{"url":"/paper/semantic-instance-segmentation-with-a","title":"Semantic Instance Segmentation with a Discriminative Loss Function","date":"2017-08-08","arxiv_id":"1708.02551","repositories_listed":8,"syntology":{"n":2,"n_ran":0,"n_unverified":2,"n_pointer_only":2}},{"url":"/paper/cross-domain-complementary-learning-with","title":"Cross-Domain Complementary Learning Using Pose for Multi-Person Part Segmentation","date":"2019-07-11","arxiv_id":"1907.05193","repositories_listed":3,"syntology":null},{"url":"/paper/parsing-r-cnn-for-instance-level-human","title":"Parsing R-CNN for Instance-Level Human Analysis","date":"2018-11-30","arxiv_id":"1811.12596","repositories_listed":2,"syntology":{"n":7,"n_ran":2,"n_unverified":5,"n_pointer_only":0}},{"url":"/paper/understanding-humans-in-crowded-scenes-deep","title":"Understanding Humans in Crowded Scenes: Deep Nested Adversarial Learning and A New Benchmark for Multi-Human Parsing","date":"2018-04-10","arxiv_id":"1804.03287","repositories_listed":2,"syntology":null},{"url":"/paper/multiple-human-parsing-in-the-wild","title":"Multiple-Human Parsing in the Wild","date":"2017-05-19","arxiv_id":"1705.07206","repositories_listed":2,"syntology":null},{"url":"/paper/instance-aware-semantic-segmentation-via","title":"Instance-aware Semantic Segmentation via Multi-task Network Cascades","date":"2015-12-14","arxiv_id":"1512.04412","repositories_listed":2,"syntology":null},{"url":"/paper/uniparser-multi-human-parsing-with-unified","title":"UniParser: Multi-Human Parsing with Unified Correlation Representation Learning","date":"2023-10-13","arxiv_id":"2310.08984","repositories_listed":1,"syntology":null},{"url":"/paper/single-stage-multi-human-parsing-via-point","title":"Single-stage Multi-human Parsing via Point Sets and Center-based Offsets","date":"2023-04-22","arxiv_id":"2304.11356","repositories_listed":1,"syntology":null},{"url":"/paper/holistic-instance-level-human-parsing","title":"Holistic, Instance-Level Human Parsing","date":"2017-09-11","arxiv_id":"1709.03612","repositories_listed":1,"syntology":null}],"syntology_records":3,"syntology_note":"a paper without a record is not a recorded non-run: it may lack an arXiv id or simply be absent from the graph layer"},"description_links":{"kept":0,"unwrapped_to_text":0,"bare_urls_linked":0,"relative_images_dropped":0,"rule":"internal links are kept only when the target slug exists in the catalog"},"syntology":{"read_at":"2026-09-24T18:15:14+00:00","claim":"Per-sample execution status on synthesized fixtures ('ran N of M samples'); not a correctness claim and not a ranking signal.","status_vocabulary":{"ran_honours":"ran, honoured the contract we drafted","ran_violates":"ran, violated the contract we drafted","ran_draft_wrong":"ran; our contract draft was wrong, not the code","ran_fixture":"ran; our fixture could not drive it","ran":"ran on a synthesized input","unverified":"unverified (harvested, no recorded run)"}},"not_shown":{"libraries":"the archive has no per-task library table","trend_sparklines":"the Trend column of the benchmarks table was a rendered image; it is not in the archive","social_and_latest_sorts":"stars and social signals are not in the archive"}}