{"id":"https://openalex.org/W4416251298","doi":"https://doi.org/10.1109/ijcnn64981.2025.11228973","title":"Make \"V\" and \"Q\" Inseparable: Deliberately Dual-Channel Adversarial Learning for Robust Visual Question Answering","display_name":"Make \"V\" and \"Q\" Inseparable: Deliberately Dual-Channel Adversarial Learning for Robust Visual Question Answering","publication_year":2025,"publication_date":"2025-06-30","ids":{"openalex":"https://openalex.org/W4416251298","doi":"https://doi.org/10.1109/ijcnn64981.2025.11228973"},"language":null,"primary_location":{"id":"doi:10.1109/ijcnn64981.2025.11228973","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11228973","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","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/A5042344822","display_name":"Hanxiao Wu","orcid":"https://orcid.org/0000-0001-7710-2177"},"institutions":[{"id":"https://openalex.org/I196699116","display_name":"Wuhan University of Technology","ror":"https://ror.org/03fe7t173","country_code":"CN","type":"education","lineage":["https://openalex.org/I196699116"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Hanxiao Wu","raw_affiliation_strings":["Wuhan University of Technology,School of Computer Science and Artifical Intelligence,Wuhan,China"],"affiliations":[{"raw_affiliation_string":"Wuhan University of Technology,School of Computer Science and Artifical Intelligence,Wuhan,China","institution_ids":["https://openalex.org/I196699116"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103248433","display_name":"Zhaowen Li","orcid":"https://orcid.org/0000-0001-6424-8038"},"institutions":[{"id":"https://openalex.org/I2250955327","display_name":"Huawei Technologies (China)","ror":"https://ror.org/00cmhce21","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250955327"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhaowen Li","raw_affiliation_strings":["Huawei Cloud,Beijing,China"],"affiliations":[{"raw_affiliation_string":"Huawei Cloud,Beijing,China","institution_ids":["https://openalex.org/I2250955327"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101667541","display_name":"Feilong Chen","orcid":"https://orcid.org/0000-0002-4860-8483"},"institutions":[{"id":"https://openalex.org/I2250955327","display_name":"Huawei Technologies (China)","ror":"https://ror.org/00cmhce21","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250955327"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Feilong Chen","raw_affiliation_strings":["Huawei Cloud,Beijing,China"],"affiliations":[{"raw_affiliation_string":"Huawei Cloud,Beijing,China","institution_ids":["https://openalex.org/I2250955327"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5114391519","display_name":"Zhiyu Wang","orcid":"https://orcid.org/0009-0008-7606-5114"},"institutions":[{"id":"https://openalex.org/I2250955327","display_name":"Huawei Technologies (China)","ror":"https://ror.org/00cmhce21","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250955327"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhiyu Wang","raw_affiliation_strings":["Huawei Cloud,Beijing,China"],"affiliations":[{"raw_affiliation_string":"Huawei Cloud,Beijing,China","institution_ids":["https://openalex.org/I2250955327"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5034350675","display_name":"Jiali Xu","orcid":"https://orcid.org/0000-0002-3022-5687"},"institutions":[{"id":"https://openalex.org/I4210138464","display_name":"Shandong Lianxing Energy Group (China)","ror":"https://ror.org/040pyq062","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210138464"]},{"id":"https://openalex.org/I4210118245","display_name":"Shandong Iron and Steel Group (China)","ror":"https://ror.org/029dmf820","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210118245"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiali Xu","raw_affiliation_strings":["ShanDong Energy Group Co., Ltd,Jinan,China"],"affiliations":[{"raw_affiliation_string":"ShanDong Energy Group Co., Ltd,Jinan,China","institution_ids":["https://openalex.org/I4210138464","https://openalex.org/I4210118245"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079401803","display_name":"Liqun Hu","orcid":"https://orcid.org/0000-0003-1772-4163"},"institutions":[{"id":"https://openalex.org/I4210138464","display_name":"Shandong Lianxing Energy Group (China)","ror":"https://ror.org/040pyq062","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210138464"]},{"id":"https://openalex.org/I4210118245","display_name":"Shandong Iron and Steel Group (China)","ror":"https://ror.org/029dmf820","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210118245"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Liquan Hu","raw_affiliation_strings":["ShanDong Energy Group Co., Ltd,Jinan,China"],"affiliations":[{"raw_affiliation_string":"ShanDong Energy Group Co., Ltd,Jinan,China","institution_ids":["https://openalex.org/I4210138464","https://openalex.org/I4210118245"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5080147176","display_name":"Huaixuan Cao","orcid":"https://orcid.org/0000-0003-0848-0339"},"institutions":[{"id":"https://openalex.org/I4210110931","display_name":"Jinan Institute of Quantum Technology","ror":"https://ror.org/02557nd11","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210110931"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Huaixuan Cao","raw_affiliation_strings":["Yunding Technology Co., Ltd,Jinan,China"],"affiliations":[{"raw_affiliation_string":"Yunding Technology Co., Ltd,Jinan,China","institution_ids":["https://openalex.org/I4210110931"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5053687147","display_name":"Yin Li","orcid":"https://orcid.org/0000-0002-4175-9000"},"institutions":[{"id":"https://openalex.org/I2250955327","display_name":"Huawei Technologies (China)","ror":"https://ror.org/00cmhce21","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250955327"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yin Li","raw_affiliation_strings":["Huawei Cloud,Beijing,China"],"affiliations":[{"raw_affiliation_string":"Huawei Cloud,Beijing,China","institution_ids":["https://openalex.org/I2250955327"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058420913","display_name":"Jinqiao Wang","orcid":"https://orcid.org/0000-0002-9118-2780"},"institutions":[{"id":"https://openalex.org/I4210094879","display_name":"Shandong Institute of Automation","ror":"https://ror.org/00qdtba35","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210094879","https://openalex.org/I4210142748"]},{"id":"https://openalex.org/I4210112150","display_name":"Institute of Automation","ror":"https://ror.org/022c3hy66","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210112150"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jinqiao Wang","raw_affiliation_strings":["Chinese Academy of Sciences,Foundation Model Research Center, Institute of Automation,Beijing,China"],"affiliations":[{"raw_affiliation_string":"Chinese Academy of Sciences,Foundation Model Research Center, Institute of Automation,Beijing,China","institution_ids":["https://openalex.org/I4210094879","https://openalex.org/I4210112150"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5038083485","display_name":"Jianlong Chang","orcid":"https://orcid.org/0000-0002-0610-907X"},"institutions":[{"id":"https://openalex.org/I2250955327","display_name":"Huawei Technologies (China)","ror":"https://ror.org/00cmhce21","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250955327"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jianlong Chang","raw_affiliation_strings":["Huawei Cloud,Beijing,China"],"affiliations":[{"raw_affiliation_string":"Huawei Cloud,Beijing,China","institution_ids":["https://openalex.org/I2250955327"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":10,"corresponding_author_ids":["https://openalex.org/A5042344822"],"corresponding_institution_ids":["https://openalex.org/I196699116"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.37433111,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9836000204086304,"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":0.9836000204086304,"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":0.004100000020116568,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.002300000051036477,"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/question-answering","display_name":"Question answering","score":0.8330000042915344},{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.7656999826431274},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.6309000253677368},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.6158000230789185},{"id":"https://openalex.org/keywords/natural-language","display_name":"Natural language","score":0.4814000129699707},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.38109999895095825},{"id":"https://openalex.org/keywords/task-analysis","display_name":"Task analysis","score":0.3513000011444092}],"concepts":[{"id":"https://openalex.org/C44291984","wikidata":"https://www.wikidata.org/wiki/Q1074173","display_name":"Question answering","level":2,"score":0.8330000042915344},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.7656999826431274},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6988000273704529},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6703000068664551},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.6309000253677368},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.6158000230789185},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5212000012397766},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.4814000129699707},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.38109999895095825},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.3513000011444092},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.34549999237060547},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.3280999958515167},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.30070000886917114},{"id":"https://openalex.org/C182306322","wikidata":"https://www.wikidata.org/wiki/Q1779371","display_name":"Order (exchange)","level":2,"score":0.2946999967098236},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.2854999899864197},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.2833000123500824},{"id":"https://openalex.org/C2776608160","wikidata":"https://www.wikidata.org/wiki/Q4785462","display_name":"Natural (archaeology)","level":2,"score":0.2703999876976013},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2615000009536743},{"id":"https://openalex.org/C2779439875","wikidata":"https://www.wikidata.org/wiki/Q1078276","display_name":"Natural language understanding","level":3,"score":0.2506999969482422}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn64981.2025.11228973","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11228973","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","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/W1933349210","https://openalex.org/W2250539671","https://openalex.org/W2745461083","https://openalex.org/W2803125506","https://openalex.org/W2963349562","https://openalex.org/W2963383024","https://openalex.org/W2963398599","https://openalex.org/W2963644680","https://openalex.org/W2964118342","https://openalex.org/W2966683369","https://openalex.org/W2970019270","https://openalex.org/W2970231061","https://openalex.org/W2983256121","https://openalex.org/W3004349648","https://openalex.org/W3034287395","https://openalex.org/W3034787499","https://openalex.org/W3035017890","https://openalex.org/W3035243232","https://openalex.org/W3035517717","https://openalex.org/W3099884329","https://openalex.org/W3101703188","https://openalex.org/W3104788521","https://openalex.org/W3110575265","https://openalex.org/W3175344799","https://openalex.org/W3177934633","https://openalex.org/W4221166941","https://openalex.org/W4293518563","https://openalex.org/W4320003009","https://openalex.org/W4385567330","https://openalex.org/W4386066382","https://openalex.org/W4391853818","https://openalex.org/W4402671600","https://openalex.org/W4403730271","https://openalex.org/W4404820176","https://openalex.org/W4405040560"],"related_works":[],"abstract_inverted_index":{"Visual":[0],"Question":[1],"Answering":[2],"(VQA)":[3],"is":[4,142],"a":[5,55,129],"challenging":[6],"task":[7],"due":[8],"to":[9,17,34,62,70,85,89,120,133,148],"the":[10,15,20,87,94,101,107,122,135,160,168,176],"vision-language":[11],"biases":[12],"which":[13,68],"restrict":[14],"model":[16,88],"sufficiently":[18],"learn":[19],"multi-modal":[21],"knowledge":[22],"from":[23,73],"visual":[24],"image":[25],"and":[26,65,76,111,144],"natural":[27],"language":[28,40],"simultaneously.":[29],"Several":[30],"recent":[31],"works":[32],"attempt":[33],"alleviate":[35],"this":[36,51],"problem":[37,123],"via":[38],"weakening":[39],"prior":[41,72],"but":[42],"ignore":[43],"vision":[44,75],"prior,":[45],"hindering":[46],"further":[47],"performance":[48,161],"improvement.":[49],"In":[50,118],"paper,":[52],"we":[53],"propose":[54],"novel":[56],"Deliberately":[57],"Dual-Channel":[58],"Adversarial":[59],"Learning":[60],"(DCAL)":[61],"make":[63],"\"V\"":[64],"\"Q\"":[66],"inseparable,":[67],"aims":[69],"weaken":[71],"both":[74],"language.":[77],"Specifically,":[78],"DCAL":[79,99,127,141,157],"introduces":[80],"in-batch":[81],"random":[82,115],"negative":[83,116],"sampling":[84],"force":[86],"be":[90,146],"wrong":[91,95],"when":[92],"given":[93],"questions":[96],"or":[97],"images.":[98],"maximizes":[100],"likelihood":[102],"of":[103,124,162],"correct":[104],"answers":[105],"for":[106,114],"original":[108],"question-image":[109,137],"pairs":[110],"minimizes":[112],"it":[113],"samples.":[117],"order":[119],"solve":[121],"false":[125],"negatives,":[126],"exploits":[128],"deliberate":[130],"training":[131],"strategy":[132],"utilize":[134],"sampled":[136],"pairs.":[138],"The":[139],"proposed":[140,156],"model-agnostic":[143],"can":[145],"applied":[147],"various":[149],"VQA":[150,165,178],"models.":[151],"Experiments":[152],"demonstrate":[153],"that":[154],"our":[155],"method":[158],"improves":[159],"existing":[163],"robust":[164],"models":[166],"on":[167,175],"sensitive":[169],"VQA-CP":[170],"dataset":[171],"while":[172],"performing":[173],"robustly":[174],"balanced":[177],"v2":[179],"dataset.":[180]},"counts_by_year":[],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2025-11-14T00:00:00"}
