{"id":"https://openalex.org/W4402981952","doi":"https://doi.org/10.1109/icme57554.2024.10688270","title":"Enhancing Out-of-Distribution Generalization in VQA through Gini Impurity-guided Adaptive Margin Loss","display_name":"Enhancing Out-of-Distribution Generalization in VQA through Gini Impurity-guided Adaptive Margin Loss","publication_year":2024,"publication_date":"2024-07-15","ids":{"openalex":"https://openalex.org/W4402981952","doi":"https://doi.org/10.1109/icme57554.2024.10688270"},"language":"en","primary_location":{"id":"doi:10.1109/icme57554.2024.10688270","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/icme57554.2024.10688270","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Multimedia and Expo (ICME)","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/A5082726995","display_name":"Shuwen Yang","orcid":"https://orcid.org/0000-0001-7728-162X"},"institutions":[{"id":"https://openalex.org/I66867065","display_name":"East China Normal University","ror":"https://ror.org/02n96ep67","country_code":"CN","type":"education","lineage":["https://openalex.org/I66867065"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Shuwen Yang","raw_affiliation_strings":["East China Normal University,Shanghai,China"],"affiliations":[{"raw_affiliation_string":"East China Normal University,Shanghai,China","institution_ids":["https://openalex.org/I66867065"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5092652527","display_name":"Tianyu Huai","orcid":"https://orcid.org/0009-0000-3573-9962"},"institutions":[{"id":"https://openalex.org/I66867065","display_name":"East China Normal University","ror":"https://ror.org/02n96ep67","country_code":"CN","type":"education","lineage":["https://openalex.org/I66867065"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tianyu Huai","raw_affiliation_strings":["East China Normal University,Shanghai,China"],"affiliations":[{"raw_affiliation_string":"East China Normal University,Shanghai,China","institution_ids":["https://openalex.org/I66867065"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5114236982","display_name":"Anran Wu","orcid":null},"institutions":[{"id":"https://openalex.org/I66867065","display_name":"East China Normal University","ror":"https://ror.org/02n96ep67","country_code":"CN","type":"education","lineage":["https://openalex.org/I66867065"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Anran Wu","raw_affiliation_strings":["East China Normal University,Shanghai,China"],"affiliations":[{"raw_affiliation_string":"East China Normal University,Shanghai,China","institution_ids":["https://openalex.org/I66867065"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5043728985","display_name":"Xingjiao Wu","orcid":"https://orcid.org/0000-0001-9146-051X"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xingjiao Wu","raw_affiliation_strings":["Fudan University,Shanghai,China"],"affiliations":[{"raw_affiliation_string":"Fudan University,Shanghai,China","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5029957470","display_name":"Wenxin Hu","orcid":"https://orcid.org/0000-0002-0330-5200"},"institutions":[{"id":"https://openalex.org/I66867065","display_name":"East China Normal University","ror":"https://ror.org/02n96ep67","country_code":"CN","type":"education","lineage":["https://openalex.org/I66867065"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenxin Hu","raw_affiliation_strings":["East China Normal University,Shanghai,China"],"affiliations":[{"raw_affiliation_string":"East China Normal University,Shanghai,China","institution_ids":["https://openalex.org/I66867065"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5010540039","display_name":"Liang He","orcid":"https://orcid.org/0000-0002-4723-5486"},"institutions":[{"id":"https://openalex.org/I66867065","display_name":"East China Normal University","ror":"https://ror.org/02n96ep67","country_code":"CN","type":"education","lineage":["https://openalex.org/I66867065"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Liang He","raw_affiliation_strings":["East China Normal University,Shanghai,China"],"affiliations":[{"raw_affiliation_string":"East China Normal University,Shanghai,China","institution_ids":["https://openalex.org/I66867065"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5082726995"],"corresponding_institution_ids":["https://openalex.org/I66867065"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.18261448,"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":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11447","display_name":"Blind Source Separation Techniques","score":0.9853000044822693,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T11447","display_name":"Blind Source Separation Techniques","score":0.9853000044822693,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T10688","display_name":"Image and Signal Denoising Methods","score":0.984000027179718,"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/T12702","display_name":"Brain Tumor Detection and Classification","score":0.972599983215332,"subfield":{"id":"https://openalex.org/subfields/2808","display_name":"Neurology"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/margin","display_name":"Margin (machine learning)","score":0.7817357182502747},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.6282194256782532},{"id":"https://openalex.org/keywords/impurity","display_name":"Impurity","score":0.5117030143737793},{"id":"https://openalex.org/keywords/distribution","display_name":"Distribution (mathematics)","score":0.491146057844162},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.3859259784221649},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.31765878200531006},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.2125599980354309},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.1711530089378357},{"id":"https://openalex.org/keywords/mathematical-analysis","display_name":"Mathematical analysis","score":0.13122397661209106}],"concepts":[{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.7817357182502747},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.6282194256782532},{"id":"https://openalex.org/C71987851","wikidata":"https://www.wikidata.org/wiki/Q7216430","display_name":"Impurity","level":2,"score":0.5117030143737793},{"id":"https://openalex.org/C110121322","wikidata":"https://www.wikidata.org/wiki/Q865811","display_name":"Distribution (mathematics)","level":2,"score":0.491146057844162},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.3859259784221649},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.31765878200531006},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.2125599980354309},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.1711530089378357},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.13122397661209106},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icme57554.2024.10688270","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/icme57554.2024.10688270","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Multimedia and Expo (ICME)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/13","display_name":"Climate action","score":0.6299999952316284}],"awards":[],"funders":[{"id":"https://openalex.org/F4320322370","display_name":"East China Normal University","ror":"https://ror.org/02n96ep67"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W1979597421","https://openalex.org/W2007980826","https://openalex.org/W2061531152","https://openalex.org/W3002753104","https://openalex.org/W2077600819","https://openalex.org/W2142036596","https://openalex.org/W2072657027","https://openalex.org/W2600246793","https://openalex.org/W4238204885"],"abstract_inverted_index":{"In":[0,125],"the":[1,28,43,78,97,104,107,126,137],"Visual":[2],"Question":[3],"Answering":[4],"(VQA)":[5],"task":[6],"context,":[7],"most":[8],"methods":[9],"are":[10],"influenced":[11],"by":[12,77],"language":[13],"bias,":[14],"resulting":[15],"in":[16,100],"poor":[17],"performance":[18],"on":[19],"out-of-distribution":[20],"data.":[21],"Recently,":[22],"some":[23,54],"works":[24,39],"attempted":[25],"to":[26,32,53,95,109,122],"use":[27,92],"adaptive":[29],"margin":[30,50,74,98,101],"loss":[31,75],"address":[33,67],"this":[34,68],"bias":[35],"issue.":[36],"However,":[37],"these":[38],"typically":[40],"consider":[41],"only":[42],"frequency":[44],"of":[45,106],"answer":[46],"labels":[47],"when":[48],"designing":[49],"loss,":[51,102],"leading":[52],"samples":[55],"being":[56],"overly":[57],"emphasized":[58],"or":[59],"lacking":[60],"sufficient":[61],"attention":[62,105],"during":[63],"model":[64,108],"training.":[65],"To":[66],"issue,":[69],"we":[70,91],"propose":[71],"a":[72],"novel":[73],"guided":[76],"Gini-impurity":[79],"for":[80],"VQA":[81],"debiasing.":[82],"By":[83],"comprehensively":[84],"considering":[85],"label":[86],"distribution":[87],"and":[88,117],"instance":[89],"complexity,":[90],"Gini":[93],"impurity":[94],"adjust":[96],"values":[99],"balancing":[103],"different":[110],"samples.":[111],"Importantly,":[112],"our":[113,130],"method":[114],"is":[115],"plug-and-play":[116],"can":[118],"be":[119],"directly":[120],"applied":[121],"any":[123],"baseline.":[124],"VQA-CP":[127],"v2":[128],"task,":[129],"evaluation":[131],"results":[132],"across":[133],"various":[134],"baselines":[135],"surpass":[136],"current":[138],"state-of-the-art":[139],"methods.":[140]},"counts_by_year":[],"updated_date":"2025-12-27T23:08:20.325037","created_date":"2025-10-10T00:00:00"}