{"id":"https://openalex.org/W3158225545","doi":"https://doi.org/10.1109/tcyb.2021.3070881","title":"Multimodal Weibull Variational Autoencoder for Jointly Modeling Image-Text Data","display_name":"Multimodal Weibull Variational Autoencoder for Jointly Modeling Image-Text Data","publication_year":2021,"publication_date":"2021-04-28","ids":{"openalex":"https://openalex.org/W3158225545","doi":"https://doi.org/10.1109/tcyb.2021.3070881","mag":"3158225545","pmid":"https://pubmed.ncbi.nlm.nih.gov/33909580"},"language":"en","primary_location":{"id":"doi:10.1109/tcyb.2021.3070881","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tcyb.2021.3070881","pdf_url":null,"source":{"id":"https://openalex.org/S4210191041","display_name":"IEEE Transactions on Cybernetics","issn_l":"2168-2267","issn":["2168-2267","2168-2275"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Cybernetics","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"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/A5100774764","display_name":"Chaojie Wang","orcid":"https://orcid.org/0000-0002-7644-7621"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chaojie Wang","raw_affiliation_strings":["National Lab of Radar Signal Processing, Collaborative Innovation Center of Information Sensing and Understanding, Xidian University, Xi&#x2019;an, China"],"raw_orcid":"https://orcid.org/0000-0002-7644-7621","affiliations":[{"raw_affiliation_string":"National Lab of Radar Signal Processing, Collaborative Innovation Center of Information Sensing and Understanding, Xidian University, Xi&#x2019;an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100427253","display_name":"Bo Chen","orcid":"https://orcid.org/0000-0001-5151-9388"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bo Chen","raw_affiliation_strings":["National Lab of Radar Signal Processing, Collaborative Innovation Center of Information Sensing and Understanding, Xidian University, Xi&#x2019;an, China"],"raw_orcid":"https://orcid.org/0000-0001-5151-9388","affiliations":[{"raw_affiliation_string":"National Lab of Radar Signal Processing, Collaborative Innovation Center of Information Sensing and Understanding, Xidian University, Xi&#x2019;an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5033586295","display_name":"Sucheng Xiao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sucheng Xiao","raw_affiliation_strings":["Security Department, ByteDance, Guangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Security Department, ByteDance, Guangzhou, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068379071","display_name":"Zhengjue Wang","orcid":"https://orcid.org/0000-0002-1846-495X"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhengjue Wang","raw_affiliation_strings":["National Lab of Radar Signal Processing, Collaborative Innovation Center of Information Sensing and Understanding, Xidian University, Xi&#x2019;an, China"],"raw_orcid":"https://orcid.org/0000-0002-1846-495X","affiliations":[{"raw_affiliation_string":"National Lab of Radar Signal Processing, Collaborative Innovation Center of Information Sensing and Understanding, Xidian University, Xi&#x2019;an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100396885","display_name":"Hao Zhang","orcid":"https://orcid.org/0000-0002-2928-2692"},"institutions":[{"id":"https://openalex.org/I205783295","display_name":"Cornell University","ror":"https://ror.org/05bnh6r87","country_code":"US","type":"education","lineage":["https://openalex.org/I205783295"]},{"id":"https://openalex.org/I4387153466","display_name":"Weill Cornell Medicine","ror":"https://ror.org/02r109517","country_code":"US","type":"education","lineage":["https://openalex.org/I205783295","https://openalex.org/I4387153466"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hao Zhang","raw_affiliation_strings":["Department of Population Health Sciences, Weill Cornell Medicine, Tompkins, NY, USA"],"raw_orcid":"https://orcid.org/0000-0002-2928-2692","affiliations":[{"raw_affiliation_string":"Department of Population Health Sciences, Weill Cornell Medicine, Tompkins, NY, USA","institution_ids":["https://openalex.org/I205783295","https://openalex.org/I4387153466"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5109012690","display_name":"Penghui Wang","orcid":"https://orcid.org/0000-0003-4046-163X"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Penghui Wang","raw_affiliation_strings":["National Lab of Radar Signal Processing, Collaborative Innovation Center of Information Sensing and Understanding, Xidian University, Xi&#x2019;an, China"],"raw_orcid":"https://orcid.org/0000-0003-4046-163X","affiliations":[{"raw_affiliation_string":"National Lab of Radar Signal Processing, Collaborative Innovation Center of Information Sensing and Understanding, Xidian University, Xi&#x2019;an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5057073729","display_name":"Ning Han","orcid":"https://orcid.org/0000-0002-0654-6026"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ning Han","raw_affiliation_strings":["Research Room 7, Institute of Mechanical Technology, Xi&#x2019;an, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Research Room 7, Institute of Mechanical Technology, Xi&#x2019;an, China","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5010394308","display_name":"Mingyuan Zhou","orcid":"https://orcid.org/0000-0002-4253-2780"},"institutions":[{"id":"https://openalex.org/I86519309","display_name":"The University of Texas at Austin","ror":"https://ror.org/00hj54h04","country_code":"US","type":"education","lineage":["https://openalex.org/I86519309"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mingyuan Zhou","raw_affiliation_strings":["McCombs School of Business, The University of Texas at Austin, Austin, TX, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"McCombs School of Business, The University of Texas at Austin, Austin, TX, USA","institution_ids":["https://openalex.org/I86519309"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":8,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.2911,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.54176546,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":"52","issue":"10","first_page":"11156","last_page":"11171"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9998000264167786,"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":0.9998000264167786,"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":0.9994000196456909,"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":0.9993000030517578,"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/autoencoder","display_name":"Autoencoder","score":0.8194348812103271},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7412333488464355},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6517218351364136},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5681329369544983},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.49598392844200134},{"id":"https://openalex.org/keywords/latent-variable","display_name":"Latent variable","score":0.4106840491294861},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.39593884348869324},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.3210524320602417}],"concepts":[{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.8194348812103271},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7412333488464355},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6517218351364136},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5681329369544983},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.49598392844200134},{"id":"https://openalex.org/C51167844","wikidata":"https://www.wikidata.org/wiki/Q4422623","display_name":"Latent variable","level":2,"score":0.4106840491294861},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.39593884348869324},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3210524320602417}],"mesh":[{"descriptor_ui":"D000069550","descriptor_name":"Machine Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000069550","descriptor_name":"Machine Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000069550","descriptor_name":"Machine Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D007858","descriptor_name":"Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D007858","descriptor_name":"Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D007858","descriptor_name":"Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D008962","descriptor_name":"Models, Theoretical","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D008962","descriptor_name":"Models, Theoretical","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D008962","descriptor_name":"Models, Theoretical","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true}],"locations_count":2,"locations":[{"id":"doi:10.1109/tcyb.2021.3070881","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tcyb.2021.3070881","pdf_url":null,"source":{"id":"https://openalex.org/S4210191041","display_name":"IEEE Transactions on Cybernetics","issn_l":"2168-2267","issn":["2168-2267","2168-2275"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Cybernetics","raw_type":"journal-article"},{"id":"pmid:33909580","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/33909580","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE transactions on cybernetics","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4","score":0.46000000834465027}],"awards":[{"id":"https://openalex.org/G3360221606","display_name":null,"funder_award_id":"61701379","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8319798711","display_name":"\u57fa\u4e8e\u6982\u7387\u7edf\u8ba1\u6a21\u578b\u7684\u591a\u5c42\u7279\u5f81\u5b66\u4e60\u4e0e\u63a8\u7406\u6280\u672f\u7814\u7a76","funder_award_id":"61771361","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320336656","display_name":"Thousand Young Talents Program of China","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":72,"referenced_works":["https://openalex.org/W1499798934","https://openalex.org/W1686810756","https://openalex.org/W1853745982","https://openalex.org/W1880262756","https://openalex.org/W1920291194","https://openalex.org/W1950831375","https://openalex.org/W1971756996","https://openalex.org/W1981613567","https://openalex.org/W2006031689","https://openalex.org/W2011974988","https://openalex.org/W2020842694","https://openalex.org/W2049853380","https://openalex.org/W2064675550","https://openalex.org/W2072750214","https://openalex.org/W2098062695","https://openalex.org/W2107215754","https://openalex.org/W2110764733","https://openalex.org/W2111847741","https://openalex.org/W2147625498","https://openalex.org/W2147800946","https://openalex.org/W2148463593","https://openalex.org/W2151691375","https://openalex.org/W2155803963","https://openalex.org/W2157006255","https://openalex.org/W2161050705","https://openalex.org/W2162072175","https://openalex.org/W2162915993","https://openalex.org/W2164587673","https://openalex.org/W2170678468","https://openalex.org/W2216511711","https://openalex.org/W2285034650","https://openalex.org/W2326180695","https://openalex.org/W2529448042","https://openalex.org/W2557865186","https://openalex.org/W2624543814","https://openalex.org/W2630750947","https://openalex.org/W2787803546","https://openalex.org/W2788195014","https://openalex.org/W2951589572","https://openalex.org/W2951707031","https://openalex.org/W2963135265","https://openalex.org/W2963198154","https://openalex.org/W2963403868","https://openalex.org/W2963811225","https://openalex.org/W2964121744","https://openalex.org/W3103576111","https://openalex.org/W3106206248","https://openalex.org/W4237791300","https://openalex.org/W6606244218","https://openalex.org/W6629687487","https://openalex.org/W6631190155","https://openalex.org/W6637373629","https://openalex.org/W6676231585","https://openalex.org/W6681753794","https://openalex.org/W6683240801","https://openalex.org/W6685044945","https://openalex.org/W6685184952","https://openalex.org/W6685511808","https://openalex.org/W6686207219","https://openalex.org/W6686643169","https://openalex.org/W6688465353","https://openalex.org/W6727968406","https://openalex.org/W6730042849","https://openalex.org/W6739190660","https://openalex.org/W6739901393","https://openalex.org/W6747981893","https://openalex.org/W6748455135","https://openalex.org/W6752724743","https://openalex.org/W6763243348","https://openalex.org/W6764096679","https://openalex.org/W6785610064","https://openalex.org/W6785936158"],"related_works":["https://openalex.org/W3013693939","https://openalex.org/W2566616303","https://openalex.org/W2159052453","https://openalex.org/W3131327266","https://openalex.org/W2734887215","https://openalex.org/W2803255133","https://openalex.org/W4297051394","https://openalex.org/W2752972570","https://openalex.org/W2988134182","https://openalex.org/W2770818364"],"abstract_inverted_index":{"For":[0],"multimodal":[1,32,53,120,135,155,166,186],"representation":[2],"learning,":[3],"traditional":[4,84],"black-box":[5],"approaches":[6],"often":[7],"fall":[8],"short":[9],"of":[10,64,143],"extracting":[11],"interpretable":[12,31],"multilayer":[13],"hidden":[14,74],"structures,":[15],"which":[16,125],"contribute":[17],"to":[18,99,104],"visualize":[19,36],"the":[20,37,62,78,88,102,113,148],"connections":[21,70],"between":[22,41,71],"different":[23,42,65],"modalities":[24,66],"at":[25],"multiple":[26],"semantic":[27,39],"levels.":[28],"To":[29,76],"extract":[30,153],"latent":[33,106,156],"representations":[34,157],"and":[35,108,131,165,176],"hierarchial":[38],"relationships":[40],"modalities,":[43],"based":[44],"on":[45,139,184],"deep":[46],"topic":[47,85],"models,":[48],"we":[49,91],"develop":[50],"a":[51,93,118],"novel":[52,119],"Poisson":[54],"gamma":[55],"belief":[56],"network":[57,97],"(mPGBN)":[58],"that":[59,147,173],"tightly":[60],"couples":[61],"observations":[63,103],"via":[67],"imposing":[68],"sparse":[69],"their":[72,105],"modality-specific":[73],"layers.":[75],"alleviate":[77],"time-consuming":[79],"Gibbs":[80],"sampler":[81],"adopted":[82],"by":[83],"models":[86],"in":[87,117,128],"testing":[89],"stage,":[90],"construct":[92],"Weibull-based":[94],"variational":[95,122],"inference":[96],"(encoder)":[98],"directly":[100],"map":[101],"representations,":[107],"further":[109],"combine":[110],"it":[111],"with":[112],"mPGBN":[114],"(decoder),":[115],"resulting":[116],"Weibull":[121],"autoencoder":[123],"(MWVAE),":[124],"is":[126],"fast":[127],"out-of-sample":[129],"prediction":[130],"can":[132,151],"handle":[133],"large-scale":[134],"datasets.":[136],"Qualitative":[137],"evaluations":[138],"bimodal":[140],"data":[141],"consisting":[142],"image-text":[144],"pairs":[145],"show":[146],"developed":[149],"MWVAE":[150,175],"successfully":[152],"expressive":[154],"for":[158],"downstream":[159],"tasks":[160],"like":[161],"missing":[162],"modality":[163],"imputation":[164],"retrieval.":[167],"Further":[168],"extensive":[169],"quantitative":[170],"results":[171],"demonstrate":[172],"both":[174],"its":[177],"supervised":[178],"extension":[179],"sMWVAE":[180],"achieve":[181],"state-of-the-art":[182],"performance":[183],"various":[185],"benchmarks.":[187]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
