{"id":"https://openalex.org/W3153332739","doi":"https://doi.org/10.1145/3442381.3449950","title":"Mitigating Gender Bias in Captioning Systems","display_name":"Mitigating Gender Bias in Captioning Systems","publication_year":2021,"publication_date":"2021-04-19","ids":{"openalex":"https://openalex.org/W3153332739","doi":"https://doi.org/10.1145/3442381.3449950","mag":"3153332739"},"language":"en","primary_location":{"id":"doi:10.1145/3442381.3449950","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3442381.3449950","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":"Proceedings of the Web Conference 2021","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3442381.3449950","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101660251","display_name":"Ruixiang Tang","orcid":"https://orcid.org/0000-0001-6476-2336"},"institutions":[{"id":"https://openalex.org/I91045830","display_name":"Texas A&M University","ror":"https://ror.org/01f5ytq51","country_code":"US","type":"education","lineage":["https://openalex.org/I91045830"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Ruixiang Tang","raw_affiliation_strings":["Texas A&amp;M University, USA"],"affiliations":[{"raw_affiliation_string":"Texas A&amp;M University, USA","institution_ids":["https://openalex.org/I91045830"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5072191151","display_name":"Mengnan Du","orcid":"https://orcid.org/0000-0002-1614-6069"},"institutions":[{"id":"https://openalex.org/I91045830","display_name":"Texas A&M University","ror":"https://ror.org/01f5ytq51","country_code":"US","type":"education","lineage":["https://openalex.org/I91045830"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mengnan Du","raw_affiliation_strings":["Texas A&amp;M University, USA"],"affiliations":[{"raw_affiliation_string":"Texas A&amp;M University, USA","institution_ids":["https://openalex.org/I91045830"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101883510","display_name":"Yuening Li","orcid":"https://orcid.org/0000-0003-3849-5523"},"institutions":[{"id":"https://openalex.org/I91045830","display_name":"Texas A&M University","ror":"https://ror.org/01f5ytq51","country_code":"US","type":"education","lineage":["https://openalex.org/I91045830"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yuening Li","raw_affiliation_strings":["Texas A&amp;M University, USA"],"affiliations":[{"raw_affiliation_string":"Texas A&amp;M University, USA","institution_ids":["https://openalex.org/I91045830"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101916543","display_name":"Zirui Liu","orcid":"https://orcid.org/0000-0001-9062-6565"},"institutions":[{"id":"https://openalex.org/I91045830","display_name":"Texas A&M University","ror":"https://ror.org/01f5ytq51","country_code":"US","type":"education","lineage":["https://openalex.org/I91045830"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zirui Liu","raw_affiliation_strings":["Texas A&amp;M University, USA"],"affiliations":[{"raw_affiliation_string":"Texas A&amp;M University, USA","institution_ids":["https://openalex.org/I91045830"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5084497683","display_name":"Na Zou","orcid":"https://orcid.org/0000-0003-1984-795X"},"institutions":[{"id":"https://openalex.org/I91045830","display_name":"Texas A&M University","ror":"https://ror.org/01f5ytq51","country_code":"US","type":"education","lineage":["https://openalex.org/I91045830"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Na Zou","raw_affiliation_strings":["Texas A&amp;M University, USA"],"affiliations":[{"raw_affiliation_string":"Texas A&amp;M University, USA","institution_ids":["https://openalex.org/I91045830"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5068477431","display_name":"Xia Hu","orcid":"https://orcid.org/0000-0003-2234-3226"},"institutions":[{"id":"https://openalex.org/I91045830","display_name":"Texas A&M University","ror":"https://ror.org/01f5ytq51","country_code":"US","type":"education","lineage":["https://openalex.org/I91045830"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xia Hu","raw_affiliation_strings":["Texas A&amp;M University, USA"],"affiliations":[{"raw_affiliation_string":"Texas A&amp;M University, USA","institution_ids":["https://openalex.org/I91045830"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5101660251"],"corresponding_institution_ids":["https://openalex.org/I91045830"],"apc_list":null,"apc_paid":null,"fwci":3.6506,"has_fulltext":false,"cited_by_count":47,"citation_normalized_percentile":{"value":0.94416667,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"633","last_page":"645"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11714","display_name":"Multimodal Machine Learning Applications","score":1.0,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":1.0,"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":0.9904999732971191,"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/T10812","display_name":"Human Pose and Action Recognition","score":0.9664000272750854,"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/closed-captioning","display_name":"Closed captioning","score":0.98232102394104},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8047589063644409},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.6536115407943726},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5917676687240601},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5406060218811035},{"id":"https://openalex.org/keywords/benchmarking","display_name":"Benchmarking","score":0.53956139087677},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4563167989253998},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.4180063009262085},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.3717997074127197},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.2760169208049774}],"concepts":[{"id":"https://openalex.org/C157657479","wikidata":"https://www.wikidata.org/wiki/Q2367247","display_name":"Closed captioning","level":3,"score":0.98232102394104},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8047589063644409},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.6536115407943726},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5917676687240601},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5406060218811035},{"id":"https://openalex.org/C86251818","wikidata":"https://www.wikidata.org/wiki/Q816754","display_name":"Benchmarking","level":2,"score":0.53956139087677},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4563167989253998},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.4180063009262085},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.3717997074127197},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.2760169208049774},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C162853370","wikidata":"https://www.wikidata.org/wiki/Q39809","display_name":"Marketing","level":1,"score":0.0},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3442381.3449950","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3442381.3449950","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":"Proceedings of the Web Conference 2021","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3442381.3449950","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3442381.3449950","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":"Proceedings of the Web Conference 2021","raw_type":"proceedings-article"},"sustainable_development_goals":[{"score":0.4300000071525574,"id":"https://metadata.un.org/sdg/5","display_name":"Gender equality"},{"score":0.41999998688697815,"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":33,"referenced_works":["https://openalex.org/W1861492603","https://openalex.org/W1895577753","https://openalex.org/W1905882502","https://openalex.org/W1931639407","https://openalex.org/W1947481528","https://openalex.org/W1956340063","https://openalex.org/W2000632877","https://openalex.org/W2483215953","https://openalex.org/W2550925836","https://openalex.org/W2575842049","https://openalex.org/W2591804103","https://openalex.org/W2750779823","https://openalex.org/W2776207810","https://openalex.org/W2795151422","https://openalex.org/W2888166343","https://openalex.org/W2895472239","https://openalex.org/W2896348597","https://openalex.org/W2911227954","https://openalex.org/W2926555354","https://openalex.org/W2950888501","https://openalex.org/W2962735233","https://openalex.org/W2962787423","https://openalex.org/W2962851944","https://openalex.org/W2962858109","https://openalex.org/W2963084599","https://openalex.org/W2963349562","https://openalex.org/W2963457723","https://openalex.org/W2963606198","https://openalex.org/W2963612262","https://openalex.org/W2963644680","https://openalex.org/W2972568911","https://openalex.org/W2972972637","https://openalex.org/W3035447285"],"related_works":["https://openalex.org/W4210416330","https://openalex.org/W4238897586","https://openalex.org/W2775506363","https://openalex.org/W3088136942","https://openalex.org/W435179959","https://openalex.org/W4290852288","https://openalex.org/W2619091065","https://openalex.org/W2949362007","https://openalex.org/W4283207562","https://openalex.org/W4399363378"],"abstract_inverted_index":{"Image":[0,140],"captioning":[1,22,115],"has":[2],"made":[3],"substantial":[4],"progress":[5],"with":[6,171],"huge":[7,101],"supporting":[8],"image":[9,47],"collections":[10],"sourced":[11],"from":[12,100],"the":[13,43,69,83,106,131,152,179],"web.":[14],"However,":[15],"recent":[16],"studies":[17],"have":[18,88],"pointed":[19],"out":[20],"that":[21,113,163],"datasets,":[23],"such":[24],"as":[25],"COCO,":[26],"contain":[27],"gender":[28,50,65,102,118,123,157,168],"bias":[29],"found":[30],"in":[31],"web":[32],"corpora.":[33],"As":[34],"a":[35,136,172],"result,":[36],"learning":[37],"models":[38,61,116],"could":[39],"heavily":[40],"rely":[41],"on":[42,95,105,147],"learned":[44],"priors":[45],"and":[46,72,79,85,178],"context":[48],"for":[49,127],"identification,":[51],"leading":[52,120],"to":[53,62,121,150,154],"incorrect":[54],"or":[55],"even":[56],"offensive":[57],"errors.":[58],"To":[59,129],"encourage":[60,151],"learn":[63,117],"correct":[64,156],"features,":[66],"we":[67,134],"reorganize":[68],"COCO":[70],"dataset":[71],"present":[73],"two":[74],"new":[75,137],"splits":[76],"COCO-GB":[77],"V1":[78],"V2":[80],"datasets":[81,182],"where":[82],"train":[84],"test":[86,108],"sets":[87],"different":[89],"gender-context":[90],"joint":[91],"distribution.":[92],"Models":[93],"relying":[94],"contextual":[96],"cues":[97],"will":[98],"suffer":[99],"prediction":[103,124,169],"errors":[104,170],"anti-stereotypical":[107],"data.":[109],"Benchmarking":[110],"experiments":[111],"reveal":[112],"most":[114],"bias,":[119,133],"high":[122],"errors,":[125],"especially":[126],"women.":[128],"alleviate":[130],"unwanted":[132],"propose":[135],"Guided":[138],"Attention":[139],"Captioning":[141],"model":[142,153],"(GAIC)":[143],"which":[144],"provides":[145],"self-guidance":[146],"visual":[148,158],"attention":[149],"capture":[155],"evidence.":[159],"Experimental":[160],"results":[161],"validate":[162],"GAIC":[164],"can":[165],"significantly":[166],"reduce":[167],"competitive":[173],"caption":[174],"quality.":[175],"Our":[176],"codes":[177],"designed":[180],"benchmark":[181],"are":[183],"available":[184],"at":[185],"https://github.com/datamllab/Mitigating_Gender_Bias_In_Captioning_System.":[186]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":14},{"year":2022,"cited_by_count":9},{"year":2021,"cited_by_count":8},{"year":2020,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
