{"id":"https://openalex.org/W3092738584","doi":"https://doi.org/10.1145/3394171.3413813","title":"Rethinking Generative Zero-Shot Learning: An Ensemble Learning Perspective for Recognising Visual Patches","display_name":"Rethinking Generative Zero-Shot Learning: An Ensemble Learning Perspective for Recognising Visual Patches","publication_year":2020,"publication_date":"2020-10-12","ids":{"openalex":"https://openalex.org/W3092738584","doi":"https://doi.org/10.1145/3394171.3413813","mag":"3092738584"},"language":"en","primary_location":{"id":"doi:10.1145/3394171.3413813","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3394171.3413813","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 28th ACM International Conference on Multimedia","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/A5100456835","display_name":"Zhi Chen","orcid":"https://orcid.org/0000-0002-9385-144X"},"institutions":[{"id":"https://openalex.org/I165143802","display_name":"University of Queensland","ror":"https://ror.org/00rqy9422","country_code":"AU","type":"education","lineage":["https://openalex.org/I165143802"]}],"countries":["AU"],"is_corresponding":true,"raw_author_name":"Zhi Chen","raw_affiliation_strings":["The University of Queensland, Brisbane, Australia"],"affiliations":[{"raw_affiliation_string":"The University of Queensland, Brisbane, Australia","institution_ids":["https://openalex.org/I165143802"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100350745","display_name":"Sen Wang","orcid":"https://orcid.org/0000-0002-5414-8276"},"institutions":[{"id":"https://openalex.org/I165143802","display_name":"University of Queensland","ror":"https://ror.org/00rqy9422","country_code":"AU","type":"education","lineage":["https://openalex.org/I165143802"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Sen Wang","raw_affiliation_strings":["The University of Queensland, Brisbane, Australia"],"affiliations":[{"raw_affiliation_string":"The University of Queensland, Brisbane, Australia","institution_ids":["https://openalex.org/I165143802"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100338386","display_name":"Jingjing Li","orcid":"https://orcid.org/0000-0002-5504-2529"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jingjing Li","raw_affiliation_strings":["University of Electronic Science and Technology of China, Chengdu, China"],"affiliations":[{"raw_affiliation_string":"University of Electronic Science and Technology of China, Chengdu, China","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5078170935","display_name":"Zi Huang","orcid":"https://orcid.org/0000-0002-9738-4949"},"institutions":[{"id":"https://openalex.org/I165143802","display_name":"University of Queensland","ror":"https://ror.org/00rqy9422","country_code":"AU","type":"education","lineage":["https://openalex.org/I165143802"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Zi Huang","raw_affiliation_strings":["University of Queensland, Brisbane, Australia"],"affiliations":[{"raw_affiliation_string":"University of Queensland, Brisbane, Australia","institution_ids":["https://openalex.org/I165143802"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5100456835"],"corresponding_institution_ids":["https://openalex.org/I165143802"],"apc_list":null,"apc_paid":null,"fwci":3.314,"has_fulltext":false,"cited_by_count":30,"citation_normalized_percentile":{"value":0.93601447,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"3413","last_page":"3421"},"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":0.9991000294685364,"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":0.9991000294685364,"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":0.9919000267982483,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.9695000052452087,"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/discriminative-model","display_name":"Discriminative model","score":0.8996177911758423},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7114954590797424},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6838055849075317},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.571238100528717},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5459616780281067},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.5149562954902649},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.496338427066803},{"id":"https://openalex.org/keywords/voting","display_name":"Voting","score":0.4770297706127167},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"Ensemble learning","score":0.44953659176826477},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.42518773674964905},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.41391780972480774},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.36254000663757324}],"concepts":[{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.8996177911758423},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7114954590797424},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6838055849075317},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.571238100528717},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5459616780281067},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.5149562954902649},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.496338427066803},{"id":"https://openalex.org/C520049643","wikidata":"https://www.wikidata.org/wiki/Q189760","display_name":"Voting","level":3,"score":0.4770297706127167},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.44953659176826477},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.42518773674964905},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.41391780972480774},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.36254000663757324},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3394171.3413813","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3394171.3413813","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 28th ACM International Conference on Multimedia","raw_type":"proceedings-article"},{"id":"pmh:oai:research-repository.griffith.edu.au:10072/405064","is_oa":false,"landing_page_url":"http://hdl.handle.net/10072/405064","pdf_url":null,"source":{"id":"https://openalex.org/S4306402548","display_name":"Griffith Research Online (Griffith University, Queensland, Australia)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I11701301","host_organization_name":"Griffith University","host_organization_lineage":["https://openalex.org/I11701301"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"","raw_type":"Conference output"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.7400000095367432}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":37,"referenced_works":["https://openalex.org/W1797268635","https://openalex.org/W1954152232","https://openalex.org/W2044913453","https://openalex.org/W2109317801","https://openalex.org/W2155292833","https://openalex.org/W2171061940","https://openalex.org/W2289084343","https://openalex.org/W2328434568","https://openalex.org/W2435696002","https://openalex.org/W2740825418","https://openalex.org/W2799215068","https://openalex.org/W2896328740","https://openalex.org/W2897964062","https://openalex.org/W2899867883","https://openalex.org/W2905461678","https://openalex.org/W2924476266","https://openalex.org/W2942519784","https://openalex.org/W2962677366","https://openalex.org/W2963163163","https://openalex.org/W2963201933","https://openalex.org/W2963283377","https://openalex.org/W2963403868","https://openalex.org/W2963779825","https://openalex.org/W2963955958","https://openalex.org/W2963960318","https://openalex.org/W2967307187","https://openalex.org/W2979571231","https://openalex.org/W2982234480","https://openalex.org/W2982407353","https://openalex.org/W2991813857","https://openalex.org/W2996841154","https://openalex.org/W3000538487","https://openalex.org/W3002678554","https://openalex.org/W3009454144","https://openalex.org/W3036013846","https://openalex.org/W3100093508","https://openalex.org/W4301409532"],"related_works":["https://openalex.org/W2888227225","https://openalex.org/W2963402808","https://openalex.org/W2905846897","https://openalex.org/W2770426046","https://openalex.org/W2310403681","https://openalex.org/W1534961803","https://openalex.org/W2675891389","https://openalex.org/W1576360539","https://openalex.org/W1999570230","https://openalex.org/W1967616013"],"abstract_inverted_index":{"Zero-shot":[0],"learning":[1],"(ZSL)":[2],"is":[3,27],"commonly":[4],"used":[5,165],"to":[6,28,73,93,166,176,206],"address":[7,101],"the":[8,58,79,82,184,189],"very":[9],"pervasive":[10],"problem":[11],"of":[12,25,50,60,85,145,170],"predicting":[13],"unseen":[14,31,122,161],"classes":[15,123,162],"in":[16,57,78],"fine-grained":[17],"image":[18,62],"classification":[19],"and":[20,90,96,120],"other":[21],"tasks.":[22],"One":[23],"family":[24],"solutions":[26],"learn":[29],"synthesised":[30,156],"visual":[32,136],"samples":[33],"produced":[34],"by":[35,133],"generative":[36,111,152],"models":[37],"from":[38,55,138,157,188],"auxiliary":[39],"semantic":[40,75],"information,":[41],"such":[42],"as":[43],"natural":[44],"language":[45],"descriptions.":[46],"However,":[47],"for":[48,142,160],"most":[49,65],"these":[51,102],"models,":[52],"performance":[53],"suffers":[54],"noise":[56],"form":[59],"irrelevant":[61],"backgrounds.":[63],"Further,":[64],"methods":[66],"do":[67],"not":[68],"allocate":[69],"a":[70,106,125,143,201],"calculated":[71],"weight":[72,207],"each":[74,158,174,208],"patch.":[76,179],"Yet,":[77],"real":[80],"world,":[81],"discriminative":[83,135,198],"power":[84],"features":[86,119,137,155],"can":[87],"be":[88],"quantified":[89],"directly":[91],"leveraged":[92],"improve":[94],"accuracy":[95,219],"reduce":[97],"computational":[98],"complexity.":[99],"To":[100],"issues,":[103],"we":[104],"propose":[105],"novel":[107,126],"framework":[108],"called":[109],"multi-patch":[110],"adversarial":[112],"nets":[113],"(MPGAN)":[114],"that":[115,193,214],"synthesises":[116],"local":[117,147,178],"patch":[118,159,209],"labels":[121],"with":[124],"weighted":[127],"voting":[128,181],"strategy.":[129],"The":[130,154],"process":[131],"begins":[132],"generating":[134],"noisy":[139],"text":[140],"descriptions":[141],"set":[144],"predefined":[146],"patches":[148,195],"using":[149],"multiple":[150],"specialist":[151],"models.":[153],"are":[163,196],"then":[164],"construct":[167],"an":[168],"ensemble":[169],"diverse":[171],"supervised":[172],"classifiers,":[173],"corresponding":[175],"one":[177],"A":[180],"strategy":[182],"averages":[183],"probability":[185],"distributions":[186],"output":[187],"classifiers":[190],"and,":[191],"given":[192],"some":[194],"more":[197],"than":[199,220],"others,":[200],"discrimination-based":[202],"attention":[203],"mechanism":[204],"helps":[205],"accordingly.":[210],"Extensive":[211],"experiments":[212],"show":[213],"MPGAN":[215],"has":[216],"significantly":[217],"greater":[218],"state-of-the-art":[221],"methods.":[222]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":11},{"year":2021,"cited_by_count":9}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
