{"id":"https://openalex.org/W3199485894","doi":"https://doi.org/10.1145/3460231.3474233","title":"Semi-Supervised Visual Representation Learning for Fashion Compatibility","display_name":"Semi-Supervised Visual Representation Learning for Fashion Compatibility","publication_year":2021,"publication_date":"2021-09-13","ids":{"openalex":"https://openalex.org/W3199485894","doi":"https://doi.org/10.1145/3460231.3474233","mag":"3199485894"},"language":"en","primary_location":{"id":"doi:10.1145/3460231.3474233","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3460231.3474233","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Fifteenth ACM Conference on Recommender Systems","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2109.08052","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5074537472","display_name":"Ambareesh Revanur","orcid":"https://orcid.org/0000-0001-5640-0052"},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Ambareesh Revanur","raw_affiliation_strings":["Robotics Institute Carnegie Mellon University, United States"],"affiliations":[{"raw_affiliation_string":"Robotics Institute Carnegie Mellon University, United States","institution_ids":["https://openalex.org/I74973139"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5113696439","display_name":"Vijay Kumar","orcid":"https://orcid.org/0009-0005-1376-213X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Vijay Kumar","raw_affiliation_strings":["Walmart Global Tech, India"],"affiliations":[{"raw_affiliation_string":"Walmart Global Tech, India","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5110730448","display_name":"Deepthi Sharma","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Deepthi Sharma","raw_affiliation_strings":["Walmart, India"],"affiliations":[{"raw_affiliation_string":"Walmart, India","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5074537472"],"corresponding_institution_ids":["https://openalex.org/I74973139"],"apc_list":null,"apc_paid":null,"fwci":1.744,"has_fulltext":false,"cited_by_count":19,"citation_normalized_percentile":{"value":0.86856096,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"463","last_page":"472"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.9994000196456909,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.9994000196456909,"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/T10719","display_name":"3D Shape Modeling and Analysis","score":0.9869999885559082,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11448","display_name":"Face recognition and analysis","score":0.9739000201225281,"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/computer-science","display_name":"Computer science","score":0.7662888765335083},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.6792956590652466},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6182896494865417},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.5853294134140015},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.4439045786857605},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.42421865463256836},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.3730548620223999}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7662888765335083},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.6792956590652466},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6182896494865417},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.5853294134140015},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.4439045786857605},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.42421865463256836},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3730548620223999}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3460231.3474233","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3460231.3474233","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Fifteenth ACM Conference on Recommender Systems","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2109.08052","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2109.08052","pdf_url":"https://arxiv.org/pdf/2109.08052","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":"pmh:oai:arXiv.org:2109.08052","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2109.08052","pdf_url":"https://arxiv.org/pdf/2109.08052","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"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":68,"referenced_works":["https://openalex.org/W1484413656","https://openalex.org/W1522301498","https://openalex.org/W2099471712","https://openalex.org/W2135367695","https://openalex.org/W2145494108","https://openalex.org/W2153579005","https://openalex.org/W2157732827","https://openalex.org/W2187089797","https://openalex.org/W2194775991","https://openalex.org/W2200092826","https://openalex.org/W2211456655","https://openalex.org/W2293363371","https://openalex.org/W2321533354","https://openalex.org/W2593390416","https://openalex.org/W2601450892","https://openalex.org/W2625728734","https://openalex.org/W2626914210","https://openalex.org/W2750549109","https://openalex.org/W2785325870","https://openalex.org/W2795117763","https://openalex.org/W2802054933","https://openalex.org/W2804047946","https://openalex.org/W2883725317","https://openalex.org/W2912053445","https://openalex.org/W2921087533","https://openalex.org/W2943865428","https://openalex.org/W2944223741","https://openalex.org/W2948564662","https://openalex.org/W2950133940","https://openalex.org/W2950180292","https://openalex.org/W2953808574","https://openalex.org/W2963258075","https://openalex.org/W2963373786","https://openalex.org/W2964121744","https://openalex.org/W2970971581","https://openalex.org/W2978426779","https://openalex.org/W2990873191","https://openalex.org/W2992308087","https://openalex.org/W2998458548","https://openalex.org/W3005680577","https://openalex.org/W3009183760","https://openalex.org/W3009561768","https://openalex.org/W3010209648","https://openalex.org/W3013135579","https://openalex.org/W3015298734","https://openalex.org/W3023742835","https://openalex.org/W3033702017","https://openalex.org/W3034366527","https://openalex.org/W3034757648","https://openalex.org/W3034781633","https://openalex.org/W3034978746","https://openalex.org/W3035235949","https://openalex.org/W3035524453","https://openalex.org/W3035668851","https://openalex.org/W3035680157","https://openalex.org/W3046498717","https://openalex.org/W3099462466","https://openalex.org/W3109225397","https://openalex.org/W3110292420","https://openalex.org/W3157598734","https://openalex.org/W3209458476","https://openalex.org/W4200268060","https://openalex.org/W4287263085","https://openalex.org/W4287697527","https://openalex.org/W4294170691","https://openalex.org/W4295312788","https://openalex.org/W4295727797","https://openalex.org/W4320013936"],"related_works":["https://openalex.org/W2081900870","https://openalex.org/W2037549926","https://openalex.org/W2345479200","https://openalex.org/W2183306018","https://openalex.org/W2849310602","https://openalex.org/W3006008237","https://openalex.org/W2419146053","https://openalex.org/W4388890789","https://openalex.org/W2088247287","https://openalex.org/W2963903416"],"abstract_inverted_index":{"We":[0,131],"consider":[1],"the":[2,76,97,113],"problem":[3],"of":[4,112,155],"complementary":[5],"fashion":[6,17,51,67],"prediction.":[7],"Existing":[8],"approaches":[9],"focus":[10],"on":[11,75,135],"learning":[12,60],"an":[13],"embedding":[14],"space":[15],"where":[16,62],"items":[18],"from":[19],"different":[20],"categories":[21],"that":[22,110,148],"are":[23,26,119],"visually":[24],"compatible":[25],"closer":[27],"to":[28,42,69,108,121],"each":[29,81,94],"other.":[30],"However,":[31],"creating":[32],"such":[33],"labeled":[34,82,98,156],"outfits":[35,74],"is":[36],"intensive":[37],"and":[38,72,116,125,138,146],"also":[39],"not":[40],"feasible":[41],"generate":[43],"all":[44],"possible":[45],"outfit":[46,83,99],"combinations,":[47],"especially":[48],"with":[49,100,151,160],"large":[50,65],"catalogs.":[52],"In":[53],"this":[54],"work,":[55],"we":[56,63,88,104],"propose":[57],"a":[58,85,90,153],"semi-supervised":[59],"approach":[61,150],"leverage":[64],"unlabeled":[66,101],"corpus":[68],"create":[70],"pseudo-positive":[71],"pseudo-negative":[73],"fly":[77],"during":[78],"training.":[79],"For":[80],"in":[84,96],"training":[86],"batch,":[87],"obtain":[89],"pseudo-outfit":[91],"by":[92],"matching":[93],"item":[95],"items.":[102],"Additionally,":[103],"introduce":[105],"consistency":[106],"regularization":[107],"ensure":[109],"representation":[111],"original":[114],"images":[115],"their":[117],"transformations":[118],"consistent":[120],"implicitly":[122],"incorporate":[123],"colour":[124],"other":[126],"important":[127],"attributes":[128],"through":[129],"self-supervision.":[130],"conduct":[132],"extensive":[133],"experiments":[134],"Polyvore,":[136],"Polyvore-D":[137],"our":[139,149],"newly":[140],"created":[141],"large-scale":[142],"Fashion":[143],"Outfits":[144],"datasets,":[145],"show":[147],"only":[152],"fraction":[154],"examples":[157],"performs":[158],"on-par":[159],"completely":[161],"supervised":[162],"methods.":[163]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":6},{"year":2022,"cited_by_count":9},{"year":2021,"cited_by_count":1}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2025-10-10T00:00:00"}
