{"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/self-correction-for-human-parsing","title":"Self-Correction for Human Parsing","arxiv_id":"1910.09777","date":"2019-10-22","proceeding":null,"authors":["Peike Li","Yunqiu Xu","Yunchao Wei","Yi Yang"],"abstract":"Labeling pixel-level masks for fine-grained semantic segmentation tasks, e.g. human parsing, remains a challenging task. The ambiguous boundary between different semantic parts and those categories with similar appearance usually are confusing, leading to unexpected noises in ground truth masks. To tackle the problem of learning with label noises, this work introduces a purification strategy, called Self-Correction for Human Parsing (SCHP), to progressively promote the reliability of the supervised labels as well as the learned models. In particular, starting from a model trained with inaccurate annotations as initialization, we design a cyclically learning scheduler to infer more reliable pseudo-masks by iteratively aggregating the current learned model with the former optimal one in an online manner. Besides, those correspondingly corrected labels can in turn to further boost the model performance. In this way, the models and the labels will reciprocally become more robust and accurate during the self-correction learning cycles. Benefiting from the superiority of SCHP, we achieve the best performance on two popular single-person human parsing benchmarks, including LIP and Pascal-Person-Part datasets. Our overall system ranks 1st in CVPR2019 LIP Challenge. Code is available at https://github.com/PeikeLi/Self-Correction-Human-Parsing.","url_abs":"https://arxiv.org/abs/1910.09777v1","url_pdf":"https://arxiv.org/pdf/1910.09777v1.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":"self-correction-for-human-parsing","repo_url":"https://github.com/PeikeLi/Self-Correction-Human-Parsing","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"self-correction-for-human-parsing","repo_url":"https://github.com/MR3z4/SemanticSegmentation","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"human-parsing","task_name":"Human Parsing"},{"task_slug":"human-part-segmentation","task_name":"Human Part Segmentation"},{"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/human-parsing-on-4d-dress","task":"Human Parsing","dataset":"4D-DRESS","model":"SCHP_Inner","rank_in_archive_order":4,"of":6,"metrics":{"mAcc":"0.908","mIoU":"0.832"},"uses_additional_data":true},{"leaderboard":"/sota/human-parsing-on-4d-dress","task":"Human Parsing","dataset":"4D-DRESS","model":"SCHP_Outer","rank_in_archive_order":6,"of":6,"metrics":{"mAcc":"0.863","mIoU":"0.768"},"uses_additional_data":true},{"leaderboard":"/sota/human-part-segmentation-on-cihp","task":"Human Part Segmentation","dataset":"CIHP","model":"ResNet101","rank_in_archive_order":4,"of":6,"metrics":{"Mean IoU":"67.47"},"uses_additional_data":false},{"leaderboard":"/sota/human-part-segmentation-on-pascal-person-part","task":"Human Part Segmentation","dataset":"PASCAL-Part","model":"SCHP","rank_in_archive_order":2,"of":7,"metrics":{"mIoU":"71.46"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1910.09777","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1910.09777"}},"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. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/MR3z4/SemanticSegmentation","reach":null},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/PeikeLi/Self-Correction-Human-Parsing","reach":{"status":"ok","spdx":"MIT"}}],"summary":{"ran_violates":1,"ran_honours":1,"unverified":1},"by_repo_kind":{"official":{"samples":2,"ran":1,"repositories":1},"listed":{"samples":1,"ran":1,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"2ba40a6b64a21878","entry":"check_bn","repo":"MR3z4/SemanticSegmentation","repo_kind":"listed","path":"utils/schp.py","file_url":"https://github.com/MR3z4/SemanticSegmentation/blob/HEAD/utils/schp.py","link_basis":"first_harvest_node","language":"python","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"2ba40a6b64a21878"}},{"code_sha256_prefix":"26664d79586aad5a","entry":"get_palette","repo":"PeikeLi/Self-Correction-Human-Parsing","repo_kind":"official","path":"mhp_extension/global_local_parsing/global_local_evaluate.py","file_url":"https://github.com/PeikeLi/Self-Correction-Human-Parsing/blob/HEAD/mhp_extension/global_local_parsing/global_local_evaluate.py","link_basis":"harvester_set","language":"python","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"26664d79586aad5a"}},{"code_sha256_prefix":"560264b5ef119a80","entry":"multi_scale_testing","repo":"PeikeLi/Self-Correction-Human-Parsing","repo_kind":"official","path":"mhp_extension/global_local_parsing/global_local_evaluate.py","file_url":"https://github.com/PeikeLi/Self-Correction-Human-Parsing/blob/HEAD/mhp_extension/global_local_parsing/global_local_evaluate.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"560264b5ef119a80"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}