{"id":"https://openalex.org/W4206126198","doi":"https://doi.org/10.1109/smc52423.2021.9658773","title":"A Semi-supervised Learning Approach for Visual Question Answering based on Maximal Correlation","display_name":"A Semi-supervised Learning Approach for Visual Question Answering based on Maximal Correlation","publication_year":2021,"publication_date":"2021-10-17","ids":{"openalex":"https://openalex.org/W4206126198","doi":"https://doi.org/10.1109/smc52423.2021.9658773"},"language":"en","primary_location":{"id":"doi:10.1109/smc52423.2021.9658773","is_oa":false,"landing_page_url":"https://doi.org/10.1109/smc52423.2021.9658773","pdf_url":null,"source":{"id":"https://openalex.org/S4363607761","display_name":"2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","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 Systems, Man, and Cybernetics (SMC)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5003351146","display_name":"Sikai Yin","orcid":null},"institutions":[{"id":"https://openalex.org/I4210114105","display_name":"Tsinghua\u2013Berkeley Shenzhen Institute","ror":"https://ror.org/02hhwwz98","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210114105","https://openalex.org/I95457486","https://openalex.org/I99065089"]},{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Sikai Yin","raw_affiliation_strings":["DSIT Research Center, Tsinghua-Berkeley Shenzhen Institute, Tsinghua University, Shenzhen, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"DSIT Research Center, Tsinghua-Berkeley Shenzhen Institute, Tsinghua University, Shenzhen, China","institution_ids":["https://openalex.org/I4210114105","https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5069637938","display_name":"Fei Ma","orcid":"https://orcid.org/0000-0003-4906-6142"},"institutions":[{"id":"https://openalex.org/I4210114105","display_name":"Tsinghua\u2013Berkeley Shenzhen Institute","ror":"https://ror.org/02hhwwz98","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210114105","https://openalex.org/I95457486","https://openalex.org/I99065089"]},{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fei Ma","raw_affiliation_strings":["DSIT Research Center, Tsinghua-Berkeley Shenzhen Institute, Tsinghua University, Shenzhen, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"DSIT Research Center, Tsinghua-Berkeley Shenzhen Institute, Tsinghua University, Shenzhen, China","institution_ids":["https://openalex.org/I4210114105","https://openalex.org/I99065089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5088293566","display_name":"Shao\u2010Lun Huang","orcid":"https://orcid.org/0000-0003-2827-4022"},"institutions":[{"id":"https://openalex.org/I4210114105","display_name":"Tsinghua\u2013Berkeley Shenzhen Institute","ror":"https://ror.org/02hhwwz98","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210114105","https://openalex.org/I95457486","https://openalex.org/I99065089"]},{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shao-Lun Huang","raw_affiliation_strings":["DSIT Research Center, Tsinghua-Berkeley Shenzhen Institute, Tsinghua University, Shenzhen, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"DSIT Research Center, Tsinghua-Berkeley Shenzhen Institute, Tsinghua University, Shenzhen, China","institution_ids":["https://openalex.org/I4210114105","https://openalex.org/I99065089"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"3152","last_page":"3157"},"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":0.9970999956130981,"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":0.996399998664856,"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/discriminative-model","display_name":"Discriminative model","score":0.7916423082351685},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6849717497825623},{"id":"https://openalex.org/keywords/cross-entropy","display_name":"Cross entropy","score":0.6311017274856567},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.626621425151825},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6193615794181824},{"id":"https://openalex.org/keywords/correlation","display_name":"Correlation","score":0.605705201625824},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.5279271602630615},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.49688270688056946},{"id":"https://openalex.org/keywords/question-answering","display_name":"Question answering","score":0.46714115142822266},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.46625885367393494},{"id":"https://openalex.org/keywords/supervised-learning","display_name":"Supervised learning","score":0.458988755941391},{"id":"https://openalex.org/keywords/correlation-coefficient","display_name":"Correlation coefficient","score":0.44645488262176514},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.22983235120773315},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.19154024124145508}],"concepts":[{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.7916423082351685},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6849717497825623},{"id":"https://openalex.org/C167981619","wikidata":"https://www.wikidata.org/wiki/Q1685498","display_name":"Cross entropy","level":3,"score":0.6311017274856567},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.626621425151825},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6193615794181824},{"id":"https://openalex.org/C117220453","wikidata":"https://www.wikidata.org/wiki/Q5172842","display_name":"Correlation","level":2,"score":0.605705201625824},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.5279271602630615},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.49688270688056946},{"id":"https://openalex.org/C44291984","wikidata":"https://www.wikidata.org/wiki/Q1074173","display_name":"Question answering","level":2,"score":0.46714115142822266},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.46625885367393494},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.458988755941391},{"id":"https://openalex.org/C2780092901","wikidata":"https://www.wikidata.org/wiki/Q3433612","display_name":"Correlation coefficient","level":2,"score":0.44645488262176514},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.22983235120773315},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.19154024124145508},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","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},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"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/smc52423.2021.9658773","is_oa":false,"landing_page_url":"https://doi.org/10.1109/smc52423.2021.9658773","pdf_url":null,"source":{"id":"https://openalex.org/S4363607761","display_name":"2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","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 Systems, Man, and Cybernetics (SMC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","score":0.75,"id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W1861492603","https://openalex.org/W1931639407","https://openalex.org/W1933349210","https://openalex.org/W1995228946","https://openalex.org/W2018582985","https://openalex.org/W2107327484","https://openalex.org/W2142192571","https://openalex.org/W2174492417","https://openalex.org/W2250539671","https://openalex.org/W2463565445","https://openalex.org/W2529436507","https://openalex.org/W2560730294","https://openalex.org/W2898130205","https://openalex.org/W2963767133","https://openalex.org/W2963954913","https://openalex.org/W2966140490","https://openalex.org/W2966225492","https://openalex.org/W3000458685","https://openalex.org/W3090957216","https://openalex.org/W3093521632","https://openalex.org/W3162399999","https://openalex.org/W3162791757","https://openalex.org/W3172448037","https://openalex.org/W6639102338","https://openalex.org/W6685520387","https://openalex.org/W6719057275","https://openalex.org/W6730782440","https://openalex.org/W6797324431"],"related_works":["https://openalex.org/W4389116644","https://openalex.org/W2153315159","https://openalex.org/W3103844505","https://openalex.org/W259157601","https://openalex.org/W4205463238","https://openalex.org/W2761785940","https://openalex.org/W2384605597","https://openalex.org/W1482209366","https://openalex.org/W4285469074","https://openalex.org/W3207683741"],"abstract_inverted_index":{"In":[0],"this":[1,112],"paper,":[2],"we":[3,32],"propose":[4,33],"a":[5,34,41],"semi-supervised":[6,35,52,113],"learning":[7,114],"approach":[8,43,106],"for":[9,111],"the":[10,23,57,86,92],"Visual":[11],"Question":[12],"Answering":[13],"(VQA)":[14],"task":[15],"based":[16,44],"on":[17,45,85],"maximal":[18,48],"correlation.":[19],"Instead":[20],"of":[21,64,98],"training":[22,42,91],"VQA":[24,65,72,87,93],"model":[25,53,73,94],"with":[26,95],"just":[27],"classification":[28],"loss":[29,36],"like":[30],"cross-entropy,":[31],"function":[37],"to":[38,50,70],"incorporate":[39],"Soft-HGR,":[40,56],"Hirschfeld-Gebelein-R\u00e9nyi":[46],"(HGR)":[47],"correlation,":[49],"realize":[51],"training.":[54],"With":[55],"high-order":[58],"correlation":[59],"from":[60,79],"cross-modal":[61],"common":[62],"information":[63],"image-question":[66],"pairs":[67],"is":[68,107],"utilized":[69],"improve":[71],"performance":[74],"even":[75],"without":[76],"discriminative":[77],"supervision":[78],"answer":[80],"labels.":[81],"We":[82],"conduct":[83],"experiments":[84],"v2":[88],"dataset":[89],"by":[90],"different":[96],"percentages":[97],"unlabeled":[99],"samples.":[100],"Experimental":[101],"results":[102],"show":[103],"that":[104],"our":[105],"efficient":[108],"and":[109],"model-agnostic":[110],"task.":[115]},"counts_by_year":[{"year":2022,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
