{"id":"https://openalex.org/W3009656642","doi":"https://doi.org/10.1109/lsp.2020.2977498","title":"Generalized Zero-Shot Learning With Multi-Channel Gaussian Mixture VAE","display_name":"Generalized Zero-Shot Learning With Multi-Channel Gaussian Mixture VAE","publication_year":2020,"publication_date":"2020-01-01","ids":{"openalex":"https://openalex.org/W3009656642","doi":"https://doi.org/10.1109/lsp.2020.2977498","mag":"3009656642"},"language":"en","primary_location":{"id":"doi:10.1109/lsp.2020.2977498","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lsp.2020.2977498","pdf_url":null,"source":{"id":"https://openalex.org/S120629676","display_name":"IEEE Signal Processing Letters","issn_l":"1070-9908","issn":["1070-9908","1558-2361"],"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 Signal Processing Letters","raw_type":"journal-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/A5072350518","display_name":"Jie Shao","orcid":"https://orcid.org/0000-0003-2615-1555"},"institutions":[{"id":"https://openalex.org/I23632641","display_name":"Shanghai University of Electric Power","ror":"https://ror.org/02w4tny03","country_code":"CN","type":"education","lineage":["https://openalex.org/I23632641"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jie Shao","raw_affiliation_strings":["School of Electronics and Information Engineering, Shanghai University of Electric Power, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"School of Electronics and Information Engineering, Shanghai University of Electric Power, Shanghai, China","institution_ids":["https://openalex.org/I23632641"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5076549816","display_name":"Xiaorui Li","orcid":"https://orcid.org/0000-0002-0274-0972"},"institutions":[{"id":"https://openalex.org/I23632641","display_name":"Shanghai University of Electric Power","ror":"https://ror.org/02w4tny03","country_code":"CN","type":"education","lineage":["https://openalex.org/I23632641"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaorui Li","raw_affiliation_strings":["School of Electronics and Information Engineering, Shanghai University of Electric Power, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"School of Electronics and Information Engineering, Shanghai University of Electric Power, Shanghai, China","institution_ids":["https://openalex.org/I23632641"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5072350518"],"corresponding_institution_ids":["https://openalex.org/I23632641"],"apc_list":null,"apc_paid":null,"fwci":1.5073,"has_fulltext":false,"cited_by_count":22,"citation_normalized_percentile":{"value":0.86002574,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":"27","issue":null,"first_page":"456","last_page":"460"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9995999932289124,"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"}},"topics":[{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9995999932289124,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9861999750137329,"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/computer-science","display_name":"Computer science","score":0.7314081192016602},{"id":"https://openalex.org/keywords/mixture-model","display_name":"Mixture model","score":0.6768320798873901},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.6247333884239197},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5838882327079773},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5821278095245361},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.569536566734314},{"id":"https://openalex.org/keywords/hyperparameter","display_name":"Hyperparameter","score":0.5647814273834229},{"id":"https://openalex.org/keywords/channel","display_name":"Channel (broadcasting)","score":0.48880836367607117},{"id":"https://openalex.org/keywords/cognitive-neuroscience-of-visual-object-recognition","display_name":"Cognitive neuroscience of visual object recognition","score":0.4672641158103943},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.40728920698165894},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.3570704162120819}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7314081192016602},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.6768320798873901},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.6247333884239197},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5838882327079773},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5821278095245361},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.569536566734314},{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.5647814273834229},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.48880836367607117},{"id":"https://openalex.org/C64876066","wikidata":"https://www.wikidata.org/wiki/Q5141226","display_name":"Cognitive neuroscience of visual object recognition","level":3,"score":0.4672641158103943},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.40728920698165894},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.3570704162120819},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","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/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/lsp.2020.2977498","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lsp.2020.2977498","pdf_url":null,"source":{"id":"https://openalex.org/S120629676","display_name":"IEEE Signal Processing Letters","issn_l":"1070-9908","issn":["1070-9908","1558-2361"],"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 Signal Processing Letters","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education","score":0.5099999904632568}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":39,"referenced_works":["https://openalex.org/W652269744","https://openalex.org/W1514535095","https://openalex.org/W1542713999","https://openalex.org/W1902237438","https://openalex.org/W2070148066","https://openalex.org/W2098411764","https://openalex.org/W2109317801","https://openalex.org/W2123024445","https://openalex.org/W2133564696","https://openalex.org/W2134270519","https://openalex.org/W2171061940","https://openalex.org/W2334493732","https://openalex.org/W2400717490","https://openalex.org/W2552383788","https://openalex.org/W2603705233","https://openalex.org/W2611632661","https://openalex.org/W2617566453","https://openalex.org/W2745461083","https://openalex.org/W2883124384","https://openalex.org/W2889587173","https://openalex.org/W2899867883","https://openalex.org/W2945070549","https://openalex.org/W2963061446","https://openalex.org/W2963283377","https://openalex.org/W2963538198","https://openalex.org/W2963545832","https://openalex.org/W2963960318","https://openalex.org/W2964105864","https://openalex.org/W2964308564","https://openalex.org/W3143107425","https://openalex.org/W4285719527","https://openalex.org/W6600609147","https://openalex.org/W6630875275","https://openalex.org/W6632702419","https://openalex.org/W6678470764","https://openalex.org/W6679434410","https://openalex.org/W6729840497","https://openalex.org/W6738394178","https://openalex.org/W6754700304"],"related_works":["https://openalex.org/W2140186469","https://openalex.org/W4390421286","https://openalex.org/W4280563792","https://openalex.org/W4389724018","https://openalex.org/W4318719684","https://openalex.org/W3183136280","https://openalex.org/W4318559728","https://openalex.org/W2775233965","https://openalex.org/W3114716045","https://openalex.org/W1578916557"],"abstract_inverted_index":{"Generalized":[0],"Zero-shot":[1],"learning":[2],"(GZSL)":[3],"for":[4,32],"object":[5],"recognition":[6],"is":[7,40,110,190],"to":[8,61,70,76,120,140],"solve":[9],"the":[10,147,177,181,187],"problem":[11],"of":[12,35,52,130,183],"recognizing":[13],"samples":[14],"in":[15,26,37,54],"both":[16],"seen":[17,24],"and":[18,42,49,85,92,169],"unseen":[19],"classes,":[20],"while":[21],"only":[22],"having":[23],"classes":[25,36],"training.":[27],"As":[28],"labeling":[29],"abundant":[30],"examples":[31],"all":[33],"kinds":[34],"realistic":[38],"scenarios":[39],"costly":[41],"impractical,":[43],"GZSL":[44],"has":[45,88],"become":[46],"a":[47,63,106,142],"novel":[48,107],"hot":[50],"field":[51],"research":[53],"recent":[55],"years.":[56],"Most":[57],"previous":[58],"methods":[59,94],"tried":[60],"learn":[62],"fixed":[64],"one-directional":[65],"mapping,":[66],"either":[67],"from":[68,74,153],"visual":[69,77,84],"semantic":[71,75,86],"features,":[72],"or":[73],"features.":[78],"However,":[79],"recently,":[80],"cross":[81],"mapping":[82],"between":[83],"features":[87],"achieved":[89],"good":[90],"results":[91],"many":[93],"come":[95],"up":[96],"based":[97],"on":[98,159,186],"this":[99,102],"idea.":[100],"In":[101],"paper,":[103],"we":[104],"propose":[105],"model.":[108],"It":[109],"Multi-channel":[111],"Gaussian":[112,117,143],"Mixture":[113],"VAE(MCGM-VAE),":[114],"which":[115],"introduces":[116],"mixture":[118,144],"model":[119],"our":[121,157,171],"multi-modal":[122],"VAE":[123],"with":[124,135],"multiple":[125],"channels.":[126],"These":[127],"channels":[128],"are":[129],"different":[131],"weight":[132],"coefficients":[133],"following":[134],"channel-weight":[136],"layers,":[137],"so":[138],"as":[139],"produce":[141],"distribution.":[145],"Then":[146],"latent":[148],"space":[149],"could":[150],"be":[151],"generated":[152],"it.":[154],"We":[155],"evaluate":[156],"method":[158],"several":[160],"benchmark":[161],"databases,":[162],"i.e.":[163],"CUB,":[164],"SUN,":[165],"AWA1,":[166],"AWA2,":[167],"aPY":[168],"prove":[170],"approach":[172],"outperforms":[173],"state-of-the-art":[174],"methods.":[175],"Through":[176],"experimental":[178,188],"data":[179],"analysis,":[180],"impact":[182],"some":[184],"hyperparameters":[185],"performance":[189],"further":[191],"analyzed.":[192]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":5},{"year":2020,"cited_by_count":2}],"updated_date":"2026-03-05T09:29:38.588285","created_date":"2025-10-10T00:00:00"}