{"id":"https://openalex.org/W3089648729","doi":"https://doi.org/10.1109/ijcnn48605.2020.9206924","title":"Deep Representation of Hierarchical Semantic Attributes for Zero-shot Learning","display_name":"Deep Representation of Hierarchical Semantic Attributes for Zero-shot Learning","publication_year":2020,"publication_date":"2020-07-01","ids":{"openalex":"https://openalex.org/W3089648729","doi":"https://doi.org/10.1109/ijcnn48605.2020.9206924","mag":"3089648729"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn48605.2020.9206924","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn48605.2020.9206924","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 International Joint Conference on Neural Networks (IJCNN)","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/A5004745750","display_name":"Zhaocheng Zhang","orcid":null},"institutions":[{"id":"https://openalex.org/I78988378","display_name":"Renmin University of China","ror":"https://ror.org/041pakw92","country_code":"CN","type":"education","lineage":["https://openalex.org/I78988378"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Zhaocheng Zhang","raw_affiliation_strings":["School of Information, Renmin University of China, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Information, Renmin University of China, Beijing, China","institution_ids":["https://openalex.org/I78988378"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5002627939","display_name":"Gang Yang","orcid":"https://orcid.org/0000-0003-0765-6769"},"institutions":[{"id":"https://openalex.org/I78988378","display_name":"Renmin University of China","ror":"https://ror.org/041pakw92","country_code":"CN","type":"education","lineage":["https://openalex.org/I78988378"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Gang Yang","raw_affiliation_strings":["Key Lab of Data Engineering and Knowledge Engineering, Renmin University of China, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Key Lab of Data Engineering and Knowledge Engineering, Renmin University of China, Beijing, China","institution_ids":["https://openalex.org/I78988378"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5004745750"],"corresponding_institution_ids":["https://openalex.org/I78988378"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.11650316,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"1","issue":null,"first_page":"1","last_page":"8"},"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":0.9994999766349792,"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":0.9994999766349792,"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":0.98089998960495,"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/T11775","display_name":"COVID-19 diagnosis using AI","score":0.9718000292778015,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7924356460571289},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.6384226083755493},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6305820345878601},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5863528251647949},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5699594616889954},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.5308701992034912},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5024402141571045},{"id":"https://openalex.org/keywords/word-embedding","display_name":"Word embedding","score":0.48181405663490295},{"id":"https://openalex.org/keywords/projection","display_name":"Projection (relational algebra)","score":0.46187329292297363},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.45583486557006836},{"id":"https://openalex.org/keywords/semantic-gap","display_name":"Semantic gap","score":0.42007166147232056},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.39340028166770935},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3343523144721985},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.22352898120880127},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.16473165154457092},{"id":"https://openalex.org/keywords/image-retrieval","display_name":"Image retrieval","score":0.09521284699440002}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7924356460571289},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.6384226083755493},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6305820345878601},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5863528251647949},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5699594616889954},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.5308701992034912},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5024402141571045},{"id":"https://openalex.org/C2777462759","wikidata":"https://www.wikidata.org/wiki/Q18395344","display_name":"Word embedding","level":3,"score":0.48181405663490295},{"id":"https://openalex.org/C57493831","wikidata":"https://www.wikidata.org/wiki/Q3134666","display_name":"Projection (relational algebra)","level":2,"score":0.46187329292297363},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.45583486557006836},{"id":"https://openalex.org/C86034646","wikidata":"https://www.wikidata.org/wiki/Q474311","display_name":"Semantic gap","level":4,"score":0.42007166147232056},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.39340028166770935},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3343523144721985},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.22352898120880127},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.16473165154457092},{"id":"https://openalex.org/C1667742","wikidata":"https://www.wikidata.org/wiki/Q10927554","display_name":"Image retrieval","level":3,"score":0.09521284699440002},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","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/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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn48605.2020.9206924","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn48605.2020.9206924","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6299999952316284,"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":53,"referenced_works":["https://openalex.org/W652269744","https://openalex.org/W1032927584","https://openalex.org/W1492420801","https://openalex.org/W1614298861","https://openalex.org/W1670526751","https://openalex.org/W1797268635","https://openalex.org/W1959608418","https://openalex.org/W2017814585","https://openalex.org/W2098411764","https://openalex.org/W2099471712","https://openalex.org/W2119880843","https://openalex.org/W2123024445","https://openalex.org/W2128532956","https://openalex.org/W2134089414","https://openalex.org/W2134270519","https://openalex.org/W2141350700","https://openalex.org/W2150295085","https://openalex.org/W2171061940","https://openalex.org/W2194775991","https://openalex.org/W2250646737","https://openalex.org/W2441043183","https://openalex.org/W2552383788","https://openalex.org/W2561940122","https://openalex.org/W2611632661","https://openalex.org/W2618752949","https://openalex.org/W2772366472","https://openalex.org/W2788455775","https://openalex.org/W2883124384","https://openalex.org/W2897510511","https://openalex.org/W2950577311","https://openalex.org/W2962762077","https://openalex.org/W2962830213","https://openalex.org/W2963465381","https://openalex.org/W2963499153","https://openalex.org/W2963545832","https://openalex.org/W2964307109","https://openalex.org/W3021421747","https://openalex.org/W3100093508","https://openalex.org/W3143107425","https://openalex.org/W4320013936","https://openalex.org/W6636510571","https://openalex.org/W6637239005","https://openalex.org/W6638319203","https://openalex.org/W6640963894","https://openalex.org/W6654896396","https://openalex.org/W6678470764","https://openalex.org/W6679849079","https://openalex.org/W6682222085","https://openalex.org/W6738084857","https://openalex.org/W6740580305","https://openalex.org/W6748174377","https://openalex.org/W6776832554","https://openalex.org/W6784846827"],"related_works":["https://openalex.org/W2750434199","https://openalex.org/W4286432911","https://openalex.org/W3093943447","https://openalex.org/W3200224724","https://openalex.org/W2347374138","https://openalex.org/W2129428289","https://openalex.org/W2050635624","https://openalex.org/W2101447046","https://openalex.org/W2091753323","https://openalex.org/W2168037874"],"abstract_inverted_index":{"On":[0],"account":[1,126],"of":[2,6,35,48,80,83,119,137,158],"a":[3,111,164,202],"large":[4],"scale":[5],"dataset":[7],"need":[8,141],"to":[9,12,32,66,77,109,114,142,162,170,193,209],"be":[10,103,143],"annotated":[11,135],"fit":[13],"for":[14,167,197,206,222],"specific":[15],"tasks,":[16],"Zero-Shot":[17,226],"Learning(ZSL)":[18],"has":[19,88],"invoked":[20],"so":[21],"much":[22],"attention":[23],"and":[24,55,72,179,200,224],"got":[25],"significant":[26],"progress":[27],"in":[28,133,211],"recent":[29],"research":[30],"due":[31,76],"the":[33,46,63,68,78,106,116,134],"prevalence":[34],"deep":[36,165],"neural":[37],"networks.":[38],"At":[39],"present,":[40],"ZSL":[41,223],"is":[42],"mainly":[43],"solved":[44,91],"through":[45],"utilization":[47],"auxiliary":[49,84],"information,":[50,85],"such":[51],"as":[52,105],"semantic":[53,73,120,168],"attributes":[54,132],"text":[56],"descriptions.":[57],"And":[58],"then,":[59],"we":[60,97,123,149,189],"can":[61,102,218],"employ":[62],"mapping":[64],"method":[65,153,217],"bridge":[67],"gap":[69],"between":[70],"visual":[71,100],"space.":[74],"However,":[75],"lack":[79],"effective":[81],"use":[82,190],"this":[86],"problem":[87],"not":[89],"been":[90],"well.":[92],"Inspired":[93],"by":[94],"previous":[95],"work,":[96],"consider":[98],"that":[99,127,140,215],"space":[101,108],"used":[104],"embedding":[107,192],"get":[110,219],"stronger":[112],"ability":[113],"express":[115,176],"precise":[117,180],"characteristics":[118],"information.":[121],"Meanwhile,":[122],"take":[124],"into":[125,185],"there":[128],"are":[129],"some":[130,195],"noise":[131],"information":[136,169,181],"public":[138,230],"datasets":[139],"processed.":[144],"Based":[145],"on":[146,228],"these":[147,207],"considerations,":[148],"propose":[150,201],"an":[151],"end-to-end":[152],"with":[154],"convolutional":[155],"architecture,":[156],"instead":[157],"conventionally":[159],"linear":[160],"projection,":[161],"provide":[163],"representation":[166],"solve":[171],"ZSL.":[172],"Semantic":[173],"features":[174],"would":[175],"more":[177],"detailed":[178],"after":[182],"being":[183],"feed":[184],"our":[186,216],"method.":[187],"Besides,":[188],"word":[191],"generate":[194],"superclasses":[196,208],"original":[198],"classes":[199],"new":[203],"loss":[204],"function":[205],"assist":[210],"training.":[212],"Experiments":[213],"show":[214],"decent":[220],"improvements":[221],"Generalized":[225],"Learning(GZSL)":[227],"several":[229],"datasets.":[231]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}