{"id":"https://openalex.org/W2964911822","doi":"https://doi.org/10.24963/ijcai.2019/337","title":"Zero-shot Learning with Many Classes by High-rank Deep Embedding Networks","display_name":"Zero-shot Learning with Many Classes by High-rank Deep Embedding Networks","publication_year":2019,"publication_date":"2019-07-28","ids":{"openalex":"https://openalex.org/W2964911822","doi":"https://doi.org/10.24963/ijcai.2019/337","mag":"2964911822"},"language":"en","primary_location":{"id":"doi:10.24963/ijcai.2019/337","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2019/337","pdf_url":"https://www.ijcai.org/proceedings/2019/0337.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.ijcai.org/proceedings/2019/0337.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5035519525","display_name":"Yuchen Guo","orcid":"https://orcid.org/0000-0001-9808-9805"},"institutions":[{"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":true,"raw_author_name":"Yuchen Guo","raw_affiliation_strings":["Department of Automation, Tsinghua University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Automation, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5057732142","display_name":"Guiguang Ding","orcid":"https://orcid.org/0000-0003-0137-9975"},"institutions":[{"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":"Guiguang Ding","raw_affiliation_strings":["School of Software, Tsinghua University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Software, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5046605531","display_name":"Jungong Han","orcid":"https://orcid.org/0000-0003-4361-956X"},"institutions":[{"id":"https://openalex.org/I39555362","display_name":"University of Warwick","ror":"https://ror.org/01a77tt86","country_code":"GB","type":"education","lineage":["https://openalex.org/I39555362"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Jungong Han","raw_affiliation_strings":["WMG Data Science, University of Warwick, Coventry, UK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"WMG Data Science, University of Warwick, Coventry, UK","institution_ids":["https://openalex.org/I39555362"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5080901723","display_name":"Hang Shao","orcid":"https://orcid.org/0000-0002-1322-4789"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hang Shao","raw_affiliation_strings":["Zhejiang Future Technology Institute (Jiaxing), Zhejiang, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Zhejiang Future Technology Institute (Jiaxing), Zhejiang, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5009565631","display_name":"Xin Lou","orcid":"https://orcid.org/0000-0003-2980-5171"},"institutions":[{"id":"https://openalex.org/I2802939634","display_name":"Chinese PLA General Hospital","ror":"https://ror.org/04gw3ra78","country_code":"CN","type":"healthcare","lineage":["https://openalex.org/I2802939634"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xin Lou","raw_affiliation_strings":["Chinese PLA General Hospital, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Chinese PLA General Hospital, Beijing, China","institution_ids":["https://openalex.org/I2802939634"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5080722708","display_name":"Qionghai Dai","orcid":"https://orcid.org/0000-0001-7043-3061"},"institutions":[{"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":"Qionghai Dai","raw_affiliation_strings":["Department of Automation, Tsinghua University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Automation, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5035519525"],"corresponding_institution_ids":["https://openalex.org/I99065089"],"apc_list":null,"apc_paid":null,"fwci":0.1451,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.57537072,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"2428","last_page":"2434"},"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":0.9997000098228455,"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":0.9997000098228455,"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/T11775","display_name":"COVID-19 diagnosis using AI","score":0.906499981880188,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T12676","display_name":"Machine Learning and ELM","score":0.9031000137329102,"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/compatibility","display_name":"Compatibility (geochemistry)","score":0.6993889808654785},{"id":"https://openalex.org/keywords/bilinear-interpolation","display_name":"Bilinear interpolation","score":0.6567915678024292},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.6557356715202332},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6406679153442383},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.46193137764930725},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3720093369483948},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.3367452025413513}],"concepts":[{"id":"https://openalex.org/C2778648169","wikidata":"https://www.wikidata.org/wiki/Q967768","display_name":"Compatibility (geochemistry)","level":2,"score":0.6993889808654785},{"id":"https://openalex.org/C205203396","wikidata":"https://www.wikidata.org/wiki/Q612143","display_name":"Bilinear interpolation","level":2,"score":0.6567915678024292},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.6557356715202332},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6406679153442383},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.46193137764930725},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3720093369483948},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3367452025413513},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.0},{"id":"https://openalex.org/C17409809","wikidata":"https://www.wikidata.org/wiki/Q161764","display_name":"Geochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.24963/ijcai.2019/337","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2019/337","pdf_url":"https://www.ijcai.org/proceedings/2019/0337.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.24963/ijcai.2019/337","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2019/337","pdf_url":"https://www.ijcai.org/proceedings/2019/0337.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G187184461","display_name":null,"funder_award_id":"61571269","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G2311577451","display_name":null,"funder_award_id":"No. 61571269","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G2778187103","display_name":null,"funder_award_id":"BX20180172","funder_id":"https://openalex.org/F4320335768","funder_display_name":"National Postdoctoral Program for Innovative Talents"},{"id":"https://openalex.org/G3424790759","display_name":null,"funder_award_id":"2018YFC0807500","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7384131426","display_name":null,"funder_award_id":"2018M640131","funder_id":"https://openalex.org/F4320321543","funder_display_name":"China Postdoctoral Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320321543","display_name":"China Postdoctoral Science Foundation","ror":"https://ror.org/0426zh255"},{"id":"https://openalex.org/F4320335768","display_name":"National Postdoctoral Program for Innovative Talents","ror":null}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2964911822.pdf","grobid_xml":"https://content.openalex.org/works/W2964911822.grobid-xml"},"referenced_works_count":34,"referenced_works":["https://openalex.org/W93016980","https://openalex.org/W652269744","https://openalex.org/W1614298861","https://openalex.org/W1797268635","https://openalex.org/W1846206282","https://openalex.org/W1960364170","https://openalex.org/W2009086942","https://openalex.org/W2070148066","https://openalex.org/W2098411764","https://openalex.org/W2117539524","https://openalex.org/W2123024445","https://openalex.org/W2124033848","https://openalex.org/W2128532956","https://openalex.org/W2141350700","https://openalex.org/W2153579005","https://openalex.org/W2171061940","https://openalex.org/W2194775991","https://openalex.org/W2289084343","https://openalex.org/W2334493732","https://openalex.org/W2405223529","https://openalex.org/W2461917396","https://openalex.org/W2514265125","https://openalex.org/W2609698233","https://openalex.org/W2611632661","https://openalex.org/W2741634395","https://openalex.org/W2887241700","https://openalex.org/W2950276680","https://openalex.org/W2950577311","https://openalex.org/W2963499153","https://openalex.org/W2963537482","https://openalex.org/W2964086552","https://openalex.org/W3100093508","https://openalex.org/W4249965322","https://openalex.org/W4294170691"],"related_works":["https://openalex.org/W2165589608","https://openalex.org/W3093557575","https://openalex.org/W2354231916","https://openalex.org/W3182767856","https://openalex.org/W2162140574","https://openalex.org/W2354073393","https://openalex.org/W2361009070","https://openalex.org/W2121457398","https://openalex.org/W1915958221","https://openalex.org/W2932872266"],"abstract_inverted_index":{"Zero-shot":[0],"learning":[1],"(ZSL)":[2],"is":[3,52,62,74,128],"a":[4,37,63,82,98,117,129,144,159],"recently":[5],"emerging":[6],"research":[7],"topic":[8],"which":[9,127,162],"aims":[10],"to":[11,153],"build":[12],"classification":[13],"models":[14],"for":[15,108],"unseen":[16],"classes":[17,49,86],"with":[18,47,110,149],"knowledge":[19],"from":[20],"auxiliary":[21],"seen":[22],"classes.":[23,112],"Though":[24],"many":[25,48,111],"ZSL":[26,42,109],"works":[27],"have":[28],"shown":[29],"promising":[30],"results":[31],"on":[32,44,168],"small-scale":[33],"datasets":[34,46,170],"by":[35],"utilizing":[36],"bilinear":[38,59,134],"compatibility":[39,60,69,125,135],"function,":[40,126,136],"the":[41,58,67,89,123,133,155,177],"performance":[43],"large-scale":[45],"(say,":[50],"ImageNet)":[51],"still":[53],"unsatisfactory.":[54],"We":[55],"argue":[56],"that":[57,72],"function":[61,70,157],"low-rank":[64],"approximation":[65],"of":[66,85,88,120,132,146],"true":[68,156],"such":[71],"it":[73],"not":[75],"expressive":[76],"enough":[77],"especially":[78],"when":[79],"there":[80],"are":[81],"large":[83],"number":[84],"because":[87],"rank":[90],"limitation.":[91],"To":[92],"address":[93],"this":[94],"issue,":[95],"we":[96,115],"propose":[97,116],"novel":[99],"approach,":[100],"termed":[101],"as":[102,122],"High-rank":[103],"Deep":[104],"Embedding":[105],"Networks":[106],"(GREEN),":[107],"In":[113],"particular,":[114],"feature-dependent":[118,150],"mixture":[119,145],"softmaxes":[121],"image-class":[124],"simple":[130],"extension":[131],"but":[137],"yields":[138],"much":[139],"better":[140],"results.":[141],"It":[142],"utilizes":[143],"non-linear":[147],"transformations":[148],"latent":[151],"variables":[152],"approximate":[154],"in":[158],"high-rank":[160],"way,":[161],"makes":[163],"GREEN":[164,174],"more":[165],"expressive.":[166],"Experiments":[167],"several":[169],"including":[171],"ImageNet":[172],"demonstrate":[173],"significantly":[175],"outperforms":[176],"state-of-the-art":[178],"approaches.":[179]},"counts_by_year":[{"year":2020,"cited_by_count":1}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-10T00:00:00"}