{"id":"https://openalex.org/W2904093354","doi":"https://doi.org/10.1609/aaai.v33i01.33019316","title":"Free VQA Models from Knowledge Inertia by Pairwise Inconformity Learning","display_name":"Free VQA Models from Knowledge Inertia by Pairwise Inconformity Learning","publication_year":2019,"publication_date":"2019-07-17","ids":{"openalex":"https://openalex.org/W2904093354","doi":"https://doi.org/10.1609/aaai.v33i01.33019316","mag":"2904093354"},"language":"en","primary_location":{"id":"doi:10.1609/aaai.v33i01.33019316","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v33i01.33019316","pdf_url":"https://ojs.aaai.org/index.php/AAAI/article/download/4969/4842","source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://ojs.aaai.org/index.php/AAAI/article/download/4969/4842","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5091531008","display_name":"Yiyi Zhou","orcid":"https://orcid.org/0000-0002-5110-4526"},"institutions":[{"id":"https://openalex.org/I191208505","display_name":"Xiamen University","ror":"https://ror.org/00mcjh785","country_code":"CN","type":"education","lineage":["https://openalex.org/I191208505"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yiyi Zhou","raw_affiliation_strings":["Xiamen University"],"affiliations":[{"raw_affiliation_string":"Xiamen University","institution_ids":["https://openalex.org/I191208505"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016080094","display_name":"Rongrong Ji","orcid":"https://orcid.org/0000-0001-9163-2932"},"institutions":[{"id":"https://openalex.org/I191208505","display_name":"Xiamen University","ror":"https://ror.org/00mcjh785","country_code":"CN","type":"education","lineage":["https://openalex.org/I191208505"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Rongrong Ji","raw_affiliation_strings":["Xiamen University"],"affiliations":[{"raw_affiliation_string":"Xiamen University","institution_ids":["https://openalex.org/I191208505"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5066326238","display_name":"Jinsong Su","orcid":"https://orcid.org/0000-0001-5606-7122"},"institutions":[{"id":"https://openalex.org/I191208505","display_name":"Xiamen University","ror":"https://ror.org/00mcjh785","country_code":"CN","type":"education","lineage":["https://openalex.org/I191208505"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jinsong Su","raw_affiliation_strings":["Xiamen University"],"affiliations":[{"raw_affiliation_string":"Xiamen University","institution_ids":["https://openalex.org/I191208505"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100771966","display_name":"Xiangming Li","orcid":"https://orcid.org/0000-0003-3128-6219"},"institutions":[{"id":"https://openalex.org/I191208505","display_name":"Xiamen University","ror":"https://ror.org/00mcjh785","country_code":"CN","type":"education","lineage":["https://openalex.org/I191208505"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiangming Li","raw_affiliation_strings":["Xiamen University"],"affiliations":[{"raw_affiliation_string":"Xiamen University","institution_ids":["https://openalex.org/I191208505"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5059926864","display_name":"Xiaoshuai Sun","orcid":"https://orcid.org/0000-0003-3912-9306"},"institutions":[{"id":"https://openalex.org/I204983213","display_name":"Harbin Institute of Technology","ror":"https://ror.org/01yqg2h08","country_code":"CN","type":"education","lineage":["https://openalex.org/I204983213"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaoshuai Sun","raw_affiliation_strings":["Harbin Institute of Technology"],"affiliations":[{"raw_affiliation_string":"Harbin Institute of Technology","institution_ids":["https://openalex.org/I204983213"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5091531008"],"corresponding_institution_ids":["https://openalex.org/I191208505"],"apc_list":null,"apc_paid":null,"fwci":0.6151,"has_fulltext":true,"cited_by_count":5,"citation_normalized_percentile":{"value":0.73975482,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"33","issue":"01","first_page":"9316","last_page":"9323"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9998999834060669,"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":0.9998999834060669,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9976999759674072,"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":0.9919999837875366,"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/pairwise-comparison","display_name":"Pairwise comparison","score":0.5963034629821777},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5954142212867737},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5757943987846375},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5258491635322571},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5120576024055481},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4981391429901123},{"id":"https://openalex.org/keywords/margin","display_name":"Margin (machine learning)","score":0.4963420033454895},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.48950302600860596},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.4872781038284302},{"id":"https://openalex.org/keywords/credibility","display_name":"Credibility","score":0.4796721935272217},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.47097256779670715},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.4245573878288269},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.4172786772251129},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.32497596740722656},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.32072633504867554},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.14773640036582947}],"concepts":[{"id":"https://openalex.org/C184898388","wikidata":"https://www.wikidata.org/wiki/Q1435712","display_name":"Pairwise comparison","level":2,"score":0.5963034629821777},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5954142212867737},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5757943987846375},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5258491635322571},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5120576024055481},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4981391429901123},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.4963420033454895},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.48950302600860596},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.4872781038284302},{"id":"https://openalex.org/C2780224610","wikidata":"https://www.wikidata.org/wiki/Q1530061","display_name":"Credibility","level":2,"score":0.4796721935272217},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.47097256779670715},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.4245573878288269},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.4172786772251129},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.32497596740722656},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.32072633504867554},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.14773640036582947},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","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/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1609/aaai.v33i01.33019316","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v33i01.33019316","pdf_url":"https://ojs.aaai.org/index.php/AAAI/article/download/4969/4842","source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1609/aaai.v33i01.33019316","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v33i01.33019316","pdf_url":"https://ojs.aaai.org/index.php/AAAI/article/download/4969/4842","source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2904093354.pdf","grobid_xml":"https://content.openalex.org/works/W2904093354.grobid-xml"},"referenced_works_count":33,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1889081078","https://openalex.org/W1933349210","https://openalex.org/W2054709414","https://openalex.org/W2138621090","https://openalex.org/W2171810632","https://openalex.org/W2176212817","https://openalex.org/W2187089797","https://openalex.org/W2194775991","https://openalex.org/W2250539671","https://openalex.org/W2277195237","https://openalex.org/W2463565445","https://openalex.org/W2546696630","https://openalex.org/W2560730294","https://openalex.org/W2584723080","https://openalex.org/W2598634450","https://openalex.org/W2744822616","https://openalex.org/W2745461083","https://openalex.org/W2949197413","https://openalex.org/W2962933067","https://openalex.org/W2963143606","https://openalex.org/W2963150162","https://openalex.org/W2963383024","https://openalex.org/W2963398599","https://openalex.org/W2963672682","https://openalex.org/W2963954913","https://openalex.org/W3016211260","https://openalex.org/W3099206234","https://openalex.org/W6640773114","https://openalex.org/W6664259166","https://openalex.org/W6687483927","https://openalex.org/W6691431627","https://openalex.org/W6743099794"],"related_works":["https://openalex.org/W2024994743","https://openalex.org/W2886721024","https://openalex.org/W4304699798","https://openalex.org/W1964539106","https://openalex.org/W2319888919","https://openalex.org/W4312807709","https://openalex.org/W2951109249","https://openalex.org/W2772065891","https://openalex.org/W3045997027","https://openalex.org/W2360514150"],"abstract_inverted_index":{"In":[0,95,112],"this":[1,64,96],"paper,":[2,97],"we":[3,98,189],"uncover":[4],"the":[5,24,30,39,48,57,60,68,72,77,82,92,107,119,160,177,186,225,228,246,252],"issue":[6,44,108],"of":[7,50,59,85,109,118,149,162,202,227,248],"knowledge":[8,110,249],"inertia":[9,250],"in":[10,18,71,131,165,238,251],"visual":[11,40,69,163],"question":[12,31,129],"answering":[13],"(VQA),":[14],"which":[15,146],"commonly":[16],"exists":[17],"most":[19],"VQA":[20,51,195,204,230,254],"models":[21,25,231,255],"and":[22,88,206,216],"forces":[23],"to":[26,33,38,105,126,140],"mainly":[27],"rely":[28],"on":[29,192,210],"content":[32],"\u201cguess\u201d":[34],"answer,":[35],"without":[36],"regard":[37],"information.":[41],"Such":[42],"an":[43,127],"not":[45],"only":[46],"impairs":[47],"performance":[49],"models,":[52,205],"but":[53],"also":[54,244],"greatly":[55],"reduces":[56],"credibility":[58],"answer":[61],"prediction.":[62],"To":[63,184],"end,":[65],"simply":[66],"highlighting":[67],"features":[70,164],"model":[73,196],"is":[74,79],"undoable,":[75],"since":[76],"prediction":[78,166],"built":[80],"upon":[81,145],"joint":[83],"modeling":[84],"two":[86,211],"modalities":[87],"largely":[89],"influenced":[90],"by":[91],"data":[93],"distribution.":[94],"propose":[99],"a":[100,136,168,193,200,235],"Pairwise":[101],"Inconformity":[102],"Learning":[103],"(PIL)":[104],"tackle":[106],"inertia.":[111],"particular,":[113],"PIL":[114,158,222],"takes":[115],"full":[116],"advantage":[117],"similar":[120],"image":[121,182],"pairs":[122],"with":[123,167,234],"diverse":[124],"answers":[125,150],"identical":[128],"provided":[130],"VQA2.0":[132],"dataset.":[133],"It":[134],"builds":[135],"multi-modal":[137],"embedding":[138],"space":[139],"project":[141],"pos./neg.":[142,181],"feature":[143],"pairs,":[144],"word":[147],"vectors":[148],"are":[151],"modeled":[152],"as":[153,197,199],"anchors.":[154],"By":[155],"doing":[156],"so,":[157],"strengthens":[159],"importance":[161],"novel":[169],"dynamic-margin":[170],"based":[171],"triplet":[172],"loss":[173],"that":[174,221],"efficiently":[175],"increases":[176],"semantic":[178],"discrepancies":[179],"between":[180],"pairs.":[183],"verify":[185],"proposed":[187],"PIL,":[188],"plug":[190],"it":[191],"baseline":[194],"well":[198],"set":[201],"recent":[203],"conduct":[207],"extensive":[208],"experiments":[209],"benchmark":[212],"datasets,":[213],"i.e.,":[214],"VQA1.0":[215],"VQA2.0.":[217],"Experimental":[218],"results":[219,243],"show":[220],"can":[223],"boost":[224],"accuracy":[226],"existing":[229,253],"(1.56%-2.93%":[232],"gain)":[233],"negligible":[236],"increase":[237],"parameters":[239],"(0.85%-5.4%":[240],"parameters).":[241],"Qualitative":[242],"reveal":[245],"elimination":[247],"after":[256],"implementing":[257],"our":[258],"PIL.":[259]},"counts_by_year":[{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
