{"id":"https://openalex.org/W4224919164","doi":"https://doi.org/10.1109/icassp43922.2022.9746493","title":"VQA-BC: Robust Visual Question Answering Via Bidirectional Chaining","display_name":"VQA-BC: Robust Visual Question Answering Via Bidirectional Chaining","publication_year":2022,"publication_date":"2022-04-27","ids":{"openalex":"https://openalex.org/W4224919164","doi":"https://doi.org/10.1109/icassp43922.2022.9746493"},"language":"en","primary_location":{"id":"doi:10.1109/icassp43922.2022.9746493","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp43922.2022.9746493","pdf_url":null,"source":{"id":"https://openalex.org/S4363607702","display_name":"ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","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":"ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","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/A5102882169","display_name":"Mingrui Lao","orcid":"https://orcid.org/0000-0002-4931-6351"},"institutions":[{"id":"https://openalex.org/I121797337","display_name":"Leiden University","ror":"https://ror.org/027bh9e22","country_code":"NL","type":"education","lineage":["https://openalex.org/I121797337"]}],"countries":["NL"],"is_corresponding":true,"raw_author_name":"Mingrui Lao","raw_affiliation_strings":["Leiden University,LIACS Medialab","LIACS Medialab, Leiden University"],"affiliations":[{"raw_affiliation_string":"Leiden University,LIACS Medialab","institution_ids":["https://openalex.org/I121797337"]},{"raw_affiliation_string":"LIACS Medialab, Leiden University","institution_ids":["https://openalex.org/I121797337"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079425523","display_name":"Yanming Guo","orcid":"https://orcid.org/0000-0001-9184-5313"},"institutions":[{"id":"https://openalex.org/I170215575","display_name":"National University of Defense Technology","ror":"https://ror.org/05d2yfz11","country_code":"CN","type":"education","lineage":["https://openalex.org/I170215575"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yanming Guo","raw_affiliation_strings":["National University of Defense Technology,College of Systems Engineering","College of Systems Engineering, National University of Defense Technology"],"affiliations":[{"raw_affiliation_string":"National University of Defense Technology,College of Systems Engineering","institution_ids":["https://openalex.org/I170215575"]},{"raw_affiliation_string":"College of Systems Engineering, National University of Defense Technology","institution_ids":["https://openalex.org/I170215575"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100344333","display_name":"Wei Chen","orcid":"https://orcid.org/0000-0001-7875-4548"},"institutions":[{"id":"https://openalex.org/I121797337","display_name":"Leiden University","ror":"https://ror.org/027bh9e22","country_code":"NL","type":"education","lineage":["https://openalex.org/I121797337"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Wei Chen","raw_affiliation_strings":["Leiden University,LIACS Medialab","LIACS Medialab, Leiden University"],"affiliations":[{"raw_affiliation_string":"Leiden University,LIACS Medialab","institution_ids":["https://openalex.org/I121797337"]},{"raw_affiliation_string":"LIACS Medialab, Leiden University","institution_ids":["https://openalex.org/I121797337"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049953967","display_name":"Nan Pu","orcid":"https://orcid.org/0000-0002-2179-8301"},"institutions":[{"id":"https://openalex.org/I121797337","display_name":"Leiden University","ror":"https://ror.org/027bh9e22","country_code":"NL","type":"education","lineage":["https://openalex.org/I121797337"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Nan Pu","raw_affiliation_strings":["Leiden University,LIACS Medialab","LIACS Medialab, Leiden University"],"affiliations":[{"raw_affiliation_string":"Leiden University,LIACS Medialab","institution_ids":["https://openalex.org/I121797337"]},{"raw_affiliation_string":"LIACS Medialab, Leiden University","institution_ids":["https://openalex.org/I121797337"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5107877141","display_name":"Michael S. Lew","orcid":null},"institutions":[{"id":"https://openalex.org/I121797337","display_name":"Leiden University","ror":"https://ror.org/027bh9e22","country_code":"NL","type":"education","lineage":["https://openalex.org/I121797337"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Michael S. Lew","raw_affiliation_strings":["Leiden University,LIACS Medialab","LIACS Medialab, Leiden University"],"affiliations":[{"raw_affiliation_string":"Leiden University,LIACS Medialab","institution_ids":["https://openalex.org/I121797337"]},{"raw_affiliation_string":"LIACS Medialab, Leiden University","institution_ids":["https://openalex.org/I121797337"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5102882169"],"corresponding_institution_ids":["https://openalex.org/I121797337"],"apc_list":null,"apc_paid":null,"fwci":0.1799,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.47975189,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"4833","last_page":"4837"},"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":0.9988999962806702,"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":0.9801999926567078,"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/chaining","display_name":"Chaining","score":0.8868355751037598},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7841168642044067},{"id":"https://openalex.org/keywords/backward-chaining","display_name":"Backward chaining","score":0.7458952069282532},{"id":"https://openalex.org/keywords/debiasing","display_name":"Debiasing","score":0.7096255421638489},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.6821020245552063},{"id":"https://openalex.org/keywords/forward-chaining","display_name":"Forward chaining","score":0.6451233625411987},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5532033443450928},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.49559152126312256},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.49036216735839844},{"id":"https://openalex.org/keywords/smoothing","display_name":"Smoothing","score":0.4471141993999481},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.35899537801742554},{"id":"https://openalex.org/keywords/inference-engine","display_name":"Inference engine","score":0.33010584115982056},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.08712038397789001}],"concepts":[{"id":"https://openalex.org/C49020025","wikidata":"https://www.wikidata.org/wiki/Q1059099","display_name":"Chaining","level":2,"score":0.8868355751037598},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7841168642044067},{"id":"https://openalex.org/C129916263","wikidata":"https://www.wikidata.org/wiki/Q1141183","display_name":"Backward chaining","level":4,"score":0.7458952069282532},{"id":"https://openalex.org/C2779458634","wikidata":"https://www.wikidata.org/wiki/Q24963715","display_name":"Debiasing","level":2,"score":0.7096255421638489},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6821020245552063},{"id":"https://openalex.org/C142614401","wikidata":"https://www.wikidata.org/wiki/Q777433","display_name":"Forward chaining","level":3,"score":0.6451233625411987},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5532033443450928},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.49559152126312256},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.49036216735839844},{"id":"https://openalex.org/C3770464","wikidata":"https://www.wikidata.org/wiki/Q775963","display_name":"Smoothing","level":2,"score":0.4471141993999481},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.35899537801742554},{"id":"https://openalex.org/C46743427","wikidata":"https://www.wikidata.org/wiki/Q1341685","display_name":"Inference engine","level":3,"score":0.33010584115982056},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.08712038397789001},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C542102704","wikidata":"https://www.wikidata.org/wiki/Q183257","display_name":"Psychotherapist","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C58328972","wikidata":"https://www.wikidata.org/wiki/Q184609","display_name":"Expert system","level":2,"score":0.0},{"id":"https://openalex.org/C188147891","wikidata":"https://www.wikidata.org/wiki/Q147638","display_name":"Cognitive science","level":1,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp43922.2022.9746493","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp43922.2022.9746493","pdf_url":null,"source":{"id":"https://openalex.org/S4363607702","display_name":"ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","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":"ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":35,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W1933349210","https://openalex.org/W2250539671","https://openalex.org/W2560730294","https://openalex.org/W2745461083","https://openalex.org/W2946299408","https://openalex.org/W2949197413","https://openalex.org/W2955124656","https://openalex.org/W2962884579","https://openalex.org/W2963521239","https://openalex.org/W2963609017","https://openalex.org/W2963717374","https://openalex.org/W2966683369","https://openalex.org/W2983256121","https://openalex.org/W3015246548","https://openalex.org/W3034287395","https://openalex.org/W3035517717","https://openalex.org/W3041062282","https://openalex.org/W3095117529","https://openalex.org/W3110575265","https://openalex.org/W3158527312","https://openalex.org/W3175593095","https://openalex.org/W3177934633","https://openalex.org/W3192587235","https://openalex.org/W3202778561","https://openalex.org/W4230917753","https://openalex.org/W4289421997","https://openalex.org/W4385245566","https://openalex.org/W6620707391","https://openalex.org/W6728881024","https://openalex.org/W6739901393","https://openalex.org/W6752083267","https://openalex.org/W6754733129","https://openalex.org/W6763000187","https://openalex.org/W6764756247"],"related_works":["https://openalex.org/W188597715","https://openalex.org/W904654316","https://openalex.org/W2105600282","https://openalex.org/W2399584202","https://openalex.org/W3165080795","https://openalex.org/W2809533179","https://openalex.org/W2017044622","https://openalex.org/W2744263431","https://openalex.org/W2387415421","https://openalex.org/W2376097826"],"abstract_inverted_index":{"Current":[0],"VQA":[1,26],"models":[2,27],"are":[3],"suffering":[4],"from":[5,28,63],"the":[6,29,35,64,79,119],"problem":[7,81,121],"of":[8,31],"overdependence":[9],"on":[10,113,126,143],"language":[11,111,141],"bias,":[12],"which":[13],"severely":[14],"reduces":[15],"their":[16,42],"robustness":[17,43],"in":[18,34,82],"real-world":[19],"scenarios.":[20],"In":[21],"this":[22],"paper,":[23],"we":[24,52,87],"analyze":[25],"view":[30],"forward/backward":[32],"chaining":[33,56,98],"inference":[36],"engine,":[37],"and":[38,96],"propose":[39],"to":[40,61,67,77,93,139],"enhance":[41],"via":[44],"a":[45,54,89],"novel":[46,90],"Bidirectional":[47],"Chaining":[48],"(VQA-BC)":[49],"framework.":[50],"Specifically,":[51],"introduce":[53],"backward":[55,97],"with":[57,99,130],"hardnegative":[58],"contrastive":[59],"learning":[60],"reason":[62],"consequence":[65],"(answers)":[66],"generate":[68],"crucial":[69],"known":[70],"facts":[71],"(question-related":[72],"visual":[73],"region":[74],"features).":[75],"Furthermore,":[76],"alleviate":[78],"overconfident":[80],"answer":[83],"prediction":[84],"(forward":[85],"chaining),":[86],"present":[88],"introspective":[91],"regularization":[92],"connect":[94],"forward":[95],"label":[100],"smoothing.":[101],"Extensive":[102],"experiments":[103],"verify":[104],"that":[105],"VQA-BC":[106],"not":[107],"only":[108],"effectively":[109],"overcomes":[110],"bias":[112,142],"out-of-distribution":[114],"dataset,":[115],"but":[116],"also":[117],"alleviates":[118],"over-correct":[120],"caused":[122],"by":[123],"ensemble-based":[124],"method":[125,135],"in-distribution":[127],"dataset.":[128,146],"Compared":[129],"competitive":[131],"debiasing":[132],"strategies,":[133],"our":[134],"achieves":[136],"state-of-the-art":[137],"performance":[138],"reduce":[140],"VQA-CP":[144],"v2":[145]},"counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
