{"id":"https://openalex.org/W4205731523","doi":"https://doi.org/10.1109/bigdata52589.2021.9671774","title":"Evaluating Gender-Neutral Training Data for Automated Image Captioning","display_name":"Evaluating Gender-Neutral Training Data for Automated Image Captioning","publication_year":2021,"publication_date":"2021-12-15","ids":{"openalex":"https://openalex.org/W4205731523","doi":"https://doi.org/10.1109/bigdata52589.2021.9671774"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata52589.2021.9671774","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata52589.2021.9671774","pdf_url":null,"source":{"id":"https://openalex.org/S4363607718","display_name":"2021 IEEE International Conference on Big Data (Big Data)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5069877760","display_name":"Jack J Amend","orcid":null},"institutions":[{"id":"https://openalex.org/I84392919","display_name":"Temple University","ror":"https://ror.org/00kx1jb78","country_code":"US","type":"education","lineage":["https://openalex.org/I84392919"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Jack J Amend","raw_affiliation_strings":["Dept of Computer and Info Sciences, Temple University, Philadelphia, US"],"affiliations":[{"raw_affiliation_string":"Dept of Computer and Info Sciences, Temple University, Philadelphia, US","institution_ids":["https://openalex.org/I84392919"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5026232764","display_name":"Albatool Wazzan","orcid":"https://orcid.org/0000-0002-0181-700X"},"institutions":[{"id":"https://openalex.org/I84392919","display_name":"Temple University","ror":"https://ror.org/00kx1jb78","country_code":"US","type":"education","lineage":["https://openalex.org/I84392919"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Albatool Wazzan","raw_affiliation_strings":["Dept of Computer and Info Sciences, Temple University, Philadelphia, US"],"affiliations":[{"raw_affiliation_string":"Dept of Computer and Info Sciences, Temple University, Philadelphia, US","institution_ids":["https://openalex.org/I84392919"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5057374067","display_name":"Richard Souvenir","orcid":"https://orcid.org/0000-0002-6066-0946"},"institutions":[{"id":"https://openalex.org/I84392919","display_name":"Temple University","ror":"https://ror.org/00kx1jb78","country_code":"US","type":"education","lineage":["https://openalex.org/I84392919"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Richard Souvenir","raw_affiliation_strings":["Dept of Computer and Info Sciences, Temple University, Philadelphia, US"],"affiliations":[{"raw_affiliation_string":"Dept of Computer and Info Sciences, Temple University, Philadelphia, US","institution_ids":["https://openalex.org/I84392919"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5069877760"],"corresponding_institution_ids":["https://openalex.org/I84392919"],"apc_list":null,"apc_paid":null,"fwci":0.196,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.59043779,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1226","last_page":"1235"},"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":0.9936000108718872,"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/T10028","display_name":"Topic Modeling","score":0.9793999791145325,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/closed-captioning","display_name":"Closed captioning","score":0.9928222894668579},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7748483419418335},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.6080102920532227},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.5936906933784485},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.5785263180732727},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5486040711402893},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5141701698303223},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.47911667823791504},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.4613521099090576},{"id":"https://openalex.org/keywords/task-analysis","display_name":"Task analysis","score":0.4513494074344635},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.40985754132270813}],"concepts":[{"id":"https://openalex.org/C157657479","wikidata":"https://www.wikidata.org/wiki/Q2367247","display_name":"Closed captioning","level":3,"score":0.9928222894668579},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7748483419418335},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.6080102920532227},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.5936906933784485},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.5785263180732727},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5486040711402893},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5141701698303223},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.47911667823791504},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.4613521099090576},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.4513494074344635},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.40985754132270813},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata52589.2021.9671774","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata52589.2021.9671774","pdf_url":null,"source":{"id":"https://openalex.org/S4363607718","display_name":"2021 IEEE International Conference on Big Data (Big Data)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","score":0.550000011920929,"display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":58,"referenced_works":["https://openalex.org/W1514535095","https://openalex.org/W1522301498","https://openalex.org/W1861492603","https://openalex.org/W1895577753","https://openalex.org/W1956340063","https://openalex.org/W2123301721","https://openalex.org/W2154652894","https://openalex.org/W2506483933","https://openalex.org/W2530395818","https://openalex.org/W2552161745","https://openalex.org/W2575842049","https://openalex.org/W2604178507","https://openalex.org/W2725155646","https://openalex.org/W2745461083","https://openalex.org/W2788481061","https://openalex.org/W2803259101","https://openalex.org/W2893425640","https://openalex.org/W2895472239","https://openalex.org/W2896348597","https://openalex.org/W2950018712","https://openalex.org/W2954841306","https://openalex.org/W2962787423","https://openalex.org/W2963084599","https://openalex.org/W2963116854","https://openalex.org/W2963349562","https://openalex.org/W2963350032","https://openalex.org/W2963448089","https://openalex.org/W2963798744","https://openalex.org/W2964042428","https://openalex.org/W2982260276","https://openalex.org/W2982358316","https://openalex.org/W2987118624","https://openalex.org/W2990751682","https://openalex.org/W3034352949","https://openalex.org/W3034700241","https://openalex.org/W3036033115","https://openalex.org/W3037831233","https://openalex.org/W3047153790","https://openalex.org/W3110619060","https://openalex.org/W3120485916","https://openalex.org/W3153332739","https://openalex.org/W3163680969","https://openalex.org/W3168398407","https://openalex.org/W3206218807","https://openalex.org/W4288007632","https://openalex.org/W4320013936","https://openalex.org/W6630875275","https://openalex.org/W6631190155","https://openalex.org/W6639102338","https://openalex.org/W6678262379","https://openalex.org/W6682631176","https://openalex.org/W6721933647","https://openalex.org/W6728551298","https://openalex.org/W6740303850","https://openalex.org/W6748382702","https://openalex.org/W6767092299","https://openalex.org/W6780051348","https://openalex.org/W6898505805"],"related_works":["https://openalex.org/W3164229987","https://openalex.org/W3215212336","https://openalex.org/W4290852288","https://openalex.org/W3217388757","https://openalex.org/W3122720459","https://openalex.org/W4298897568","https://openalex.org/W4289422896","https://openalex.org/W1938708284","https://openalex.org/W4380190185","https://openalex.org/W4394050964"],"abstract_inverted_index":{"Amassing":[0],"large-scale":[1],"datasets":[2],"used":[3,50],"to":[4,118],"train":[5],"machine":[6],"learning":[7,34,45],"algorithms":[8,46],"often":[9],"includes":[10],"crowd-sourcing":[11],"or":[12,47],"web":[13],"scraping.":[14],"The":[15],"data":[16,49,60],"resulting":[17],"from":[18],"these":[19],"approaches":[20],"can":[21],"carry":[22],"undesired":[23],"societal":[24],"biases":[25],"that":[26,95],"are":[27],"reflected":[28],"in":[29,103,111],"the":[30,33,44,48,64,104,107,114],"predictions":[31],"of":[32,66,106],"system.":[35],"Recently,":[36],"researchers":[37],"have":[38],"proposed":[39],"mitigation":[40],"strategies":[41],"targeting":[42],"either":[43],"for":[51,63,74],"training.":[52],"In":[53],"this":[54,79],"paper,":[55],"we":[56],"evaluate":[57],"a":[58,101],"simple":[59],"augmentation":[61],"strategy":[62],"task":[65],"automated":[67],"image":[68,84,108],"captioning,":[69],"namely":[70],"substituting":[71],"gendered":[72],"terms":[73],"gender-neutral":[75,129],"equivalents.":[76],"We":[77,93],"evaluated":[78],"approach":[80],"on":[81],"multiple":[82],"recent":[83],"captioning":[85],"models":[86],"using":[87],"both":[88],"objective":[89],"and":[90],"subjective":[91],"analysis.":[92],"found":[94],"human":[96],"raters":[97],"did":[98],"not":[99],"find":[100],"difference":[102],"quality":[105],"captions":[109,122],"and,":[110],"some":[112],"cases,":[113],"model":[115],"was":[116],"able":[117],"generate":[119],"more":[120],"accurate":[121],"with":[123,128],"additional":[124],"detail":[125],"when":[126],"trained":[127],"data.":[130]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}