{"id":"https://openalex.org/W3122591556","doi":"https://doi.org/10.1109/tip.2021.3113780","title":"AGRNet: Adaptive Graph Representation Learning and Reasoning for Face Parsing","display_name":"AGRNet: Adaptive Graph Representation Learning and Reasoning for Face Parsing","publication_year":2021,"publication_date":"2021-01-01","ids":{"openalex":"https://openalex.org/W3122591556","doi":"https://doi.org/10.1109/tip.2021.3113780","mag":"3122591556","pmid":"https://pubmed.ncbi.nlm.nih.gov/34559650"},"language":"en","primary_location":{"id":"doi:10.1109/tip.2021.3113780","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tip.2021.3113780","pdf_url":null,"source":{"id":"https://openalex.org/S4210173141","display_name":"IEEE Transactions on Image Processing","issn_l":"1057-7149","issn":["1057-7149","1941-0042"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Image Processing","raw_type":"journal-article"},"type":"article","indexed_in":["arxiv","crossref","pubmed"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2101.07034","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5034124842","display_name":"Gusi Te","orcid":"https://orcid.org/0000-0003-2259-5490"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Gusi Te","raw_affiliation_strings":["Wangxuan Institute of Computer Technology, Peking University, No. 128, Zhongguancun North Street, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0003-2259-5490","affiliations":[{"raw_affiliation_string":"Wangxuan Institute of Computer Technology, Peking University, No. 128, Zhongguancun North Street, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5059045087","display_name":"Wei Hu","orcid":"https://orcid.org/0000-0002-9860-0922"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wei Hu","raw_affiliation_strings":["Wangxuan Institute of Computer Technology, Peking University, No. 128, Zhongguancun North Street, Beijing, China. (e-mail: forhuwei@pku.edu.cn)"],"raw_orcid":"https://orcid.org/0000-0002-9860-0922","affiliations":[{"raw_affiliation_string":"Wangxuan Institute of Computer Technology, Peking University, No. 128, Zhongguancun North Street, Beijing, China. (e-mail: forhuwei@pku.edu.cn)","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5057548821","display_name":"Yinglu Liu","orcid":"https://orcid.org/0000-0001-5670-3299"},"institutions":[{"id":"https://openalex.org/I4210103986","display_name":"Jingdong (China)","ror":"https://ror.org/01dkjkq64","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210103986"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yinglu Liu","raw_affiliation_strings":["JD AI Research, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"JD AI Research, Beijing, China","institution_ids":["https://openalex.org/I4210103986"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5086834120","display_name":"Hailin Shi","orcid":"https://orcid.org/0000-0002-3603-2683"},"institutions":[{"id":"https://openalex.org/I4210103986","display_name":"Jingdong (China)","ror":"https://ror.org/01dkjkq64","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210103986"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hailin Shi","raw_affiliation_strings":["JD AI Research, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-3603-2683","affiliations":[{"raw_affiliation_string":"JD AI Research, Beijing, China","institution_ids":["https://openalex.org/I4210103986"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5017597537","display_name":"Tao Mei","orcid":"https://orcid.org/0000-0003-2497-7732"},"institutions":[{"id":"https://openalex.org/I4210103986","display_name":"Jingdong (China)","ror":"https://ror.org/01dkjkq64","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210103986"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tao Mei","raw_affiliation_strings":["JD AI Research, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0003-2497-7732","affiliations":[{"raw_affiliation_string":"JD AI Research, Beijing, China","institution_ids":["https://openalex.org/I4210103986"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.1794,"has_fulltext":false,"cited_by_count":29,"citation_normalized_percentile":{"value":0.88325586,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":"30","issue":null,"first_page":"8236","last_page":"8250"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11448","display_name":"Face recognition and analysis","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11448","display_name":"Face recognition and analysis","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9782999753952026,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9778000116348267,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/parsing","display_name":"Parsing","score":0.7231905460357666},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6954957842826843},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.675904393196106},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.6324949860572815},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5691028237342834},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5121324062347412},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.4810388386249542},{"id":"https://openalex.org/keywords/facial-recognition-system","display_name":"Facial recognition system","score":0.4471641778945923},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3268049955368042},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.26533132791519165}],"concepts":[{"id":"https://openalex.org/C186644900","wikidata":"https://www.wikidata.org/wiki/Q194152","display_name":"Parsing","level":2,"score":0.7231905460357666},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6954957842826843},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.675904393196106},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.6324949860572815},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5691028237342834},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5121324062347412},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.4810388386249542},{"id":"https://openalex.org/C31510193","wikidata":"https://www.wikidata.org/wiki/Q1192553","display_name":"Facial recognition system","level":3,"score":0.4471641778945923},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3268049955368042},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.26533132791519165}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/tip.2021.3113780","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tip.2021.3113780","pdf_url":null,"source":{"id":"https://openalex.org/S4210173141","display_name":"IEEE Transactions on Image Processing","issn_l":"1057-7149","issn":["1057-7149","1941-0042"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Image Processing","raw_type":"journal-article"},{"id":"pmid:34559650","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/34559650","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE transactions on image processing : a publication of the IEEE Signal Processing Society","raw_type":null},{"id":"pmh:oai:arXiv.org:2101.07034","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2101.07034","pdf_url":"https://arxiv.org/pdf/2101.07034","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2101.07034","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2101.07034","pdf_url":"https://arxiv.org/pdf/2101.07034","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities","score":0.7400000095367432}],"awards":[{"id":"https://openalex.org/G3604809996","display_name":"\u57fa\u4e8e\u8c31\u56fe\u7406\u8bba\u7684\u591a\u5206\u8fa8\u7387\u56fe\u5377\u79ef\u795e\u7ecf\u7f51\u7edc\u7814\u7a76","funder_award_id":"61972009","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":82,"referenced_works":["https://openalex.org/W1796263212","https://openalex.org/W1903029394","https://openalex.org/W1905033729","https://openalex.org/W1980163762","https://openalex.org/W2129210471","https://openalex.org/W2133564696","https://openalex.org/W2194775991","https://openalex.org/W2203062554","https://openalex.org/W2295744361","https://openalex.org/W2341528187","https://openalex.org/W2412782625","https://openalex.org/W2477916117","https://openalex.org/W2519887557","https://openalex.org/W2554423077","https://openalex.org/W2560023338","https://openalex.org/W2598915960","https://openalex.org/W2607170299","https://openalex.org/W2608058963","https://openalex.org/W2611104282","https://openalex.org/W2737682710","https://openalex.org/W2742023617","https://openalex.org/W2749895778","https://openalex.org/W2776638780","https://openalex.org/W2883779225","https://openalex.org/W2890779863","https://openalex.org/W2904856314","https://openalex.org/W2954300568","https://openalex.org/W2955058313","https://openalex.org/W2956588698","https://openalex.org/W2962891704","https://openalex.org/W2963073398","https://openalex.org/W2963091558","https://openalex.org/W2963319519","https://openalex.org/W2963403868","https://openalex.org/W2963613470","https://openalex.org/W2963789946","https://openalex.org/W2963858333","https://openalex.org/W2963881378","https://openalex.org/W2964015378","https://openalex.org/W2964072977","https://openalex.org/W2964091144","https://openalex.org/W2964252655","https://openalex.org/W2964308564","https://openalex.org/W2964321699","https://openalex.org/W2969015049","https://openalex.org/W2972321983","https://openalex.org/W2979879857","https://openalex.org/W2981689412","https://openalex.org/W2981750895","https://openalex.org/W2981899103","https://openalex.org/W2986831462","https://openalex.org/W2991453701","https://openalex.org/W2993235622","https://openalex.org/W2997973210","https://openalex.org/W2998428583","https://openalex.org/W3005647825","https://openalex.org/W3009224666","https://openalex.org/W3014792118","https://openalex.org/W3022478135","https://openalex.org/W3023488494","https://openalex.org/W3025102114","https://openalex.org/W3034521057","https://openalex.org/W3034934229","https://openalex.org/W3035526186","https://openalex.org/W3043563192","https://openalex.org/W3094918035","https://openalex.org/W3101998545","https://openalex.org/W3107634219","https://openalex.org/W3134134173","https://openalex.org/W4297733535","https://openalex.org/W4385245566","https://openalex.org/W6639622156","https://openalex.org/W6645451754","https://openalex.org/W6679434410","https://openalex.org/W6720006811","https://openalex.org/W6726873649","https://openalex.org/W6729856380","https://openalex.org/W6739901393","https://openalex.org/W6754309021","https://openalex.org/W6757137537","https://openalex.org/W6767688279","https://openalex.org/W6775709611"],"related_works":["https://openalex.org/W2965546495","https://openalex.org/W4389116644","https://openalex.org/W2153315159","https://openalex.org/W3103844505","https://openalex.org/W259157601","https://openalex.org/W4205463238","https://openalex.org/W2761785940","https://openalex.org/W2110523656","https://openalex.org/W2152075398","https://openalex.org/W2084942241"],"abstract_inverted_index":{"Face":[0],"parsing":[1,84,116,159,200,242,251],"infers":[2],"a":[3,34,42,104,114,122,129,139,208],"pixel-wise":[4],"label":[5],"to":[6,68,99,146,157,191,211,223,253],"each":[7,74],"facial":[8,31,50,65,124],"component,":[9,75],"which":[10,25,144,221],"has":[11],"drawn":[12],"much":[13],"attention":[14],"recently.":[15],"Previous":[16],"methods":[17],"have":[18],"shown":[19],"their":[20],"success":[21],"in":[22,45,49,141,217],"face":[23,241],"parsing,":[24],"however":[26],"overlook":[27],"the":[28,38,77,101,110,135,142,153,162,171,181,184,195,198,218,232,236,246,249,255],"correlation":[29],"among":[30,173],"components.":[32],"As":[33],"matter":[35],"of":[36,113,197,235,257],"fact,":[37],"component-wise":[39,78],"relationship":[40,79],"is":[41],"critical":[43],"clue":[44],"discriminating":[46],"ambiguous":[47],"pixels":[48,151],"area.":[51],"To":[52,202],"address":[53],"this":[54],"issue,":[55],"we":[56,90,132,206],"propose":[57,207],"adaptive":[58,93],"graph":[59,96,105],"representation":[60],"learning":[61],"and":[62,80,94,149,168],"reasoning":[63],"over":[64,170],"components,":[66],"aiming":[67],"learn":[69],"representative":[70],"vertices":[71,179,216,225],"that":[72],"describe":[73],"exploit":[76],"thereby":[81],"produce":[82],"accurate":[83],"results":[85,160,230],"against":[86],"ambiguity.":[87],"In":[88],"particular,":[89],"devise":[91],"an":[92],"differentiable":[95],"abstraction":[97],"method":[98],"represent":[100],"components":[102,174],"on":[103,180,239,248],"via":[106],"pixel-to-vertex":[107],"projection":[108],"under":[109],"initial":[111],"condition":[112],"predicted":[115],"map,":[117],"where":[118],"pixel":[119,192],"features":[120,187],"within":[121],"certain":[123],"region":[125],"are":[126,188],"aggregated":[127],"onto":[128],"vertex.":[130],"Further,":[131],"explicitly":[133],"incorporate":[134],"image":[136],"edge":[137,148],"as":[138],"prior":[140],"model,":[143,205],"helps":[145],"discriminate":[147],"non-edge":[150],"during":[152],"projection,":[154],"thus":[155],"leading":[156],"refined":[158,185],"along":[161,244],"edges.":[163],"Then,":[164],"our":[165,204,258],"model":[166,238],"learns":[167],"reasons":[169],"relations":[172],"by":[175],"propagating":[176],"information":[177],"across":[178],"graph.":[182],"Finally,":[183],"vertex":[186],"projected":[189],"back":[190],"grids":[193],"for":[194],"prediction":[196],"final":[199],"map.":[201],"train":[203],"discriminative":[209],"loss":[210],"penalize":[212],"small":[213],"distances":[214],"between":[215],"feature":[219],"space,":[220],"leads":[222],"distinct":[224],"with":[226,245],"strong":[227],"semantics.":[228],"Experimental":[229],"show":[231],"superior":[233],"performance":[234],"proposed":[237],"multiple":[240],"datasets,":[243],"validation":[247],"human":[250],"task":[252],"demonstrate":[254],"generalizability":[256],"model.":[259]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":10},{"year":2023,"cited_by_count":8},{"year":2022,"cited_by_count":6}],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2025-10-10T00:00:00"}
