{"id":"https://openalex.org/W2912727434","doi":"https://doi.org/10.1145/3308558.3313444","title":"Dressing as a Whole: Outfit Compatibility Learning Based on Node-wise Graph Neural Networks","display_name":"Dressing as a Whole: Outfit Compatibility Learning Based on Node-wise Graph Neural Networks","publication_year":2019,"publication_date":"2019-05-13","ids":{"openalex":"https://openalex.org/W2912727434","doi":"https://doi.org/10.1145/3308558.3313444","mag":"2912727434"},"language":"en","primary_location":{"id":"doi:10.1145/3308558.3313444","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3308558.3313444","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"The World Wide Web Conference","raw_type":"proceedings-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3308558.3313444","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Zeyu Cui","orcid":null},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"funder","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210112150","display_name":"Institute of Automation","ror":"https://ror.org/022c3hy66","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210112150"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Zeyu Cui","raw_affiliation_strings":["Institute of Automation, Chinese Academy of Sciences, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Automation, Chinese Academy of Sciences, China","institution_ids":["https://openalex.org/I4210112150","https://openalex.org/I19820366"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Zekun Li","orcid":null},"institutions":[{"id":"https://openalex.org/I4210156404","display_name":"Institute of Information Engineering","ror":"https://ror.org/04r53se39","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210156404"]},{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"funder","lineage":["https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zekun Li","raw_affiliation_strings":["Institute of Information Engineering, Chinese Academy of Sciences, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Information Engineering, Chinese Academy of Sciences, China","institution_ids":["https://openalex.org/I4210156404","https://openalex.org/I19820366"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Shu Wu","orcid":null},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"funder","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210112150","display_name":"Institute of Automation","ror":"https://ror.org/022c3hy66","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210112150"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shu Wu","raw_affiliation_strings":["Institute of Automation, Chinese Academy of Sciences, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Automation, Chinese Academy of Sciences, China","institution_ids":["https://openalex.org/I4210112150","https://openalex.org/I19820366"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Xiao-Yu Zhang","orcid":null},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"funder","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210156404","display_name":"Institute of Information Engineering","ror":"https://ror.org/04r53se39","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210156404"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiao-Yu Zhang","raw_affiliation_strings":["Institute of Information Engineering, Chinese Academy of Sciences, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Information Engineering, Chinese Academy of Sciences, China","institution_ids":["https://openalex.org/I4210156404","https://openalex.org/I19820366"]}]},{"author_position":"last","author":{"id":null,"display_name":"Liang Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"funder","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210112150","display_name":"Institute of Automation","ror":"https://ror.org/022c3hy66","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210112150"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Liang Wang","raw_affiliation_strings":["Institute of Automation, Chinese Academy of Sciences, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Automation, Chinese Academy of Sciences, China","institution_ids":["https://openalex.org/I4210112150","https://openalex.org/I19820366"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I19820366","https://openalex.org/I4210112150"],"apc_list":null,"apc_paid":null,"fwci":5.5113,"has_fulltext":false,"cited_by_count":109,"citation_normalized_percentile":{"value":0.96658158,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"307","last_page":"317"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11605","display_name":"Visual Attention and Saliency Detection","score":0.9970999956130981,"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/T11605","display_name":"Visual Attention and Saliency Detection","score":0.9970999956130981,"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/T11165","display_name":"Image and Video Quality Assessment","score":0.987500011920929,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.9787999987602234,"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/compatibility","display_name":"Compatibility (geochemistry)","score":0.8026999831199646},{"id":"https://openalex.org/keywords/backward-compatibility","display_name":"Backward compatibility","score":0.4496999979019165},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.37450000643730164},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.35760000348091125}],"concepts":[{"id":"https://openalex.org/C2778648169","wikidata":"https://www.wikidata.org/wiki/Q967768","display_name":"Compatibility (geochemistry)","level":2,"score":0.8026999831199646},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6977999806404114},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.45339998602867126},{"id":"https://openalex.org/C20574231","wikidata":"https://www.wikidata.org/wiki/Q844605","display_name":"Backward compatibility","level":2,"score":0.4496999979019165},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.37450000643730164},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.35760000348091125},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.2703999876976013},{"id":"https://openalex.org/C81147070","wikidata":"https://www.wikidata.org/wiki/Q1172449","display_name":"Encapsulation (networking)","level":2,"score":0.26809999346733093},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.26260000467300415},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.25699999928474426}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3308558.3313444","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3308558.3313444","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"The World Wide Web Conference","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:1902.08009","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1902.08009","pdf_url":"https://arxiv.org/pdf/1902.08009","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":"doi:10.1145/3308558.3313444","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3308558.3313444","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"The World Wide Web Conference","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W179875071","https://openalex.org/W1501856433","https://openalex.org/W1888005072","https://openalex.org/W2021354639","https://openalex.org/W2027731328","https://openalex.org/W2036479700","https://openalex.org/W2062118960","https://openalex.org/W2064675550","https://openalex.org/W2116261113","https://openalex.org/W2116341502","https://openalex.org/W2154851992","https://openalex.org/W2171928131","https://openalex.org/W2183341477","https://openalex.org/W2221507685","https://openalex.org/W2402268235","https://openalex.org/W2483053118","https://openalex.org/W2737102415","https://openalex.org/W2742143754","https://openalex.org/W2767109396","https://openalex.org/W2774458425","https://openalex.org/W2788699718","https://openalex.org/W2962756421","https://openalex.org/W2963346996","https://openalex.org/W2963857660","https://openalex.org/W6739901393"],"related_works":[],"abstract_inverted_index":{"With":[0],"the":[1,7,32,42,59,67,85,145,172,187,198,248,257],"rapid":[2],"development":[3],"of":[4,10,27,69,84,242,251,260],"fashion":[5,13,28,44],"market,":[6],"customers'":[8],"demands":[9],"customers":[11],"for":[12],"recommendation":[14,29],"are":[15],"rising.":[16],"In":[17,105,170],"this":[18,54],"paper,":[19],"we":[20,37,96,107,152],"aim":[21],"to":[22,39,53,57,80,98,186,196,212],"investigate":[23],"a":[24,48,77,103,109,116,133,150],"practical":[25],"problem":[26,55],"by":[30,135,183],"answering":[31],"question":[33],"\u201cwhich":[34],"item":[35,236],"should":[36],"select":[38],"match":[40],"with":[41,202,239],"given":[43,252],"items":[45,71,89,137],"and":[46,118,165],"form":[47],"compatible":[49],"outfit\u201d.":[50],"The":[51],"key":[52],"is":[56,178,181,194],"estimate":[58],"outfit":[60,75,101,128,146,199,214],"compatibility.":[61],"Previous":[62],"works":[63],"which":[64,159,180],"focus":[65],"on":[66,175,229],"compatibility":[68,147,200,215,249],"two":[70,124,188,230],"or":[72,218],"represent":[73,99],"an":[74,91,100,235],"as":[76,102,132],"sequence":[78],"fail":[79],"make":[81],"full":[82],"use":[83],"complex":[86],"relations":[87],"among":[88],"in":[90],"outfit.":[92],"To":[93,143],"remedy":[94],"this,":[95],"propose":[97,153],"graph.":[104],"particular,":[106],"construct":[108],"Fashion":[110],"Graph,":[111],"where":[112],"each":[113,119,127,176],"node":[114,163,168,173,204],"represents":[115,121],"category":[117,141],"edge":[120,177],"interaction":[122,174],"between":[123],"categories.":[125],"Accordingly,":[126],"can":[129,160,207],"be":[130,210],"represented":[131],"subgraph":[134],"putting":[136],"into":[138],"their":[139],"corresponding":[140],"nodes.":[142,190],"infer":[144],"from":[148,216,223],"such":[149],"graph,":[151],"Node-wise":[154],"Graph":[155],"Neural":[156],"Networks":[157],"(NGNN)":[158],"better":[161,167],"model":[162,213],"interactions":[164],"learn":[166],"representations.":[169,205],"NGNN,":[171],"different,":[179],"determined":[182],"parameters":[184],"correlated":[185],"connected":[189],"An":[191],"attention":[192],"mechanism":[193],"utilized":[195],"calculate":[197],"score":[201],"learned":[203],"NGNN":[206],"not":[208],"only":[209],"used":[211],"visual":[217],"textual":[219],"modality":[220],"but":[221],"also":[222],"multiple":[224],"modalities.":[225],"We":[226],"conduct":[227],"experiments":[228],"tasks:":[231],"(1)":[232],"Fill-in-the-blank:":[233],"suggesting":[234],"that":[237],"matches":[238],"existing":[240],"components":[241],"outfit;":[243],"(2)":[244],"Compatibility":[245],"prediction:":[246],"predicting":[247],"scores":[250],"outfits.":[253],"Experimental":[254],"results":[255],"demonstrate":[256],"great":[258],"superiority":[259],"our":[261],"proposed":[262],"method":[263],"over":[264],"others.":[265]},"counts_by_year":[{"year":2026,"cited_by_count":5},{"year":2025,"cited_by_count":13},{"year":2024,"cited_by_count":16},{"year":2023,"cited_by_count":20},{"year":2022,"cited_by_count":31},{"year":2021,"cited_by_count":12},{"year":2020,"cited_by_count":10},{"year":2019,"cited_by_count":1},{"year":2012,"cited_by_count":1}],"updated_date":"2026-05-08T15:41:06.802602","created_date":"2019-02-21T00:00:00"}