{"id":"https://openalex.org/W4205216949","doi":"https://doi.org/10.1109/tcsii.2021.3136250","title":"Semantic-Visual Combination Propagation Network for Zero-Shot Learning","display_name":"Semantic-Visual Combination Propagation Network for Zero-Shot Learning","publication_year":2021,"publication_date":"2021-12-17","ids":{"openalex":"https://openalex.org/W4205216949","doi":"https://doi.org/10.1109/tcsii.2021.3136250"},"language":"en","primary_location":{"id":"doi:10.1109/tcsii.2021.3136250","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tcsii.2021.3136250","pdf_url":null,"source":{"id":"https://openalex.org/S93916849","display_name":"IEEE Transactions on Circuits & Systems II Express Briefs","issn_l":"1549-7747","issn":["1549-7747","1558-3791"],"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 Circuits and Systems II: Express Briefs","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/A5101460292","display_name":"Wenli Song","orcid":"https://orcid.org/0000-0001-9257-9379"},"institutions":[{"id":"https://openalex.org/I158842170","display_name":"Chongqing University","ror":"https://ror.org/023rhb549","country_code":"CN","type":"education","lineage":["https://openalex.org/I158842170"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Wenli Song","raw_affiliation_strings":["School of Microelectronics and Communication Engineering, Chongqing University, Chongqing, China"],"affiliations":[{"raw_affiliation_string":"School of Microelectronics and Communication Engineering, Chongqing University, Chongqing, China","institution_ids":["https://openalex.org/I158842170"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5106578837","display_name":"Lei Zhang","orcid":"https://orcid.org/0000-0002-5305-8543"},"institutions":[{"id":"https://openalex.org/I158842170","display_name":"Chongqing University","ror":"https://ror.org/023rhb549","country_code":"CN","type":"education","lineage":["https://openalex.org/I158842170"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lei Zhang","raw_affiliation_strings":["School of Microelectronics and Communication Engineering, Chongqing University, Chongqing, China"],"affiliations":[{"raw_affiliation_string":"School of Microelectronics and Communication Engineering, Chongqing University, Chongqing, China","institution_ids":["https://openalex.org/I158842170"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5101460292"],"corresponding_institution_ids":["https://openalex.org/I158842170"],"apc_list":null,"apc_paid":null,"fwci":1.3597,"has_fulltext":false,"cited_by_count":10,"citation_normalized_percentile":{"value":0.84964594,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":99},"biblio":{"volume":"69","issue":"4","first_page":"2341","last_page":"2345"},"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":0.9998000264167786,"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":0.9998000264167786,"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":0.9671000242233276,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9538999795913696,"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":0.7319285869598389},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.6601790189743042},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.634933352470398},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.6134070754051208},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.5503503680229187},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5356518626213074},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.5302690267562866},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5259960889816284},{"id":"https://openalex.org/keywords/semantic-feature","display_name":"Semantic feature","score":0.5021393299102783},{"id":"https://openalex.org/keywords/semantic-memory","display_name":"Semantic memory","score":0.47149723768234253},{"id":"https://openalex.org/keywords/semantic-computing","display_name":"Semantic computing","score":0.41459953784942627},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3924669027328491},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.3480052351951599},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.33667153120040894},{"id":"https://openalex.org/keywords/semantic-web","display_name":"Semantic Web","score":0.09596514701843262}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7319285869598389},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.6601790189743042},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.634933352470398},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.6134070754051208},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.5503503680229187},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5356518626213074},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.5302690267562866},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5259960889816284},{"id":"https://openalex.org/C2781122975","wikidata":"https://www.wikidata.org/wiki/Q16928266","display_name":"Semantic feature","level":2,"score":0.5021393299102783},{"id":"https://openalex.org/C197914299","wikidata":"https://www.wikidata.org/wiki/Q18650","display_name":"Semantic memory","level":3,"score":0.47149723768234253},{"id":"https://openalex.org/C511149849","wikidata":"https://www.wikidata.org/wiki/Q7449051","display_name":"Semantic computing","level":3,"score":0.41459953784942627},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3924669027328491},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.3480052351951599},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.33667153120040894},{"id":"https://openalex.org/C2129575","wikidata":"https://www.wikidata.org/wiki/Q54837","display_name":"Semantic Web","level":2,"score":0.09596514701843262},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"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/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C169900460","wikidata":"https://www.wikidata.org/wiki/Q2200417","display_name":"Cognition","level":2,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C169760540","wikidata":"https://www.wikidata.org/wiki/Q207011","display_name":"Neuroscience","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tcsii.2021.3136250","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tcsii.2021.3136250","pdf_url":null,"source":{"id":"https://openalex.org/S93916849","display_name":"IEEE Transactions on Circuits & Systems II Express Briefs","issn_l":"1549-7747","issn":["1549-7747","1558-3791"],"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 Circuits and Systems II: Express Briefs","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7699999809265137,"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities"}],"awards":[{"id":"https://openalex.org/G6051203138","display_name":null,"funder_award_id":"61771079","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"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W2194775991","https://openalex.org/W2289084343","https://openalex.org/W2552383788","https://openalex.org/W2883360306","https://openalex.org/W2901194007","https://openalex.org/W2962903908","https://openalex.org/W2963486920","https://openalex.org/W2963846885","https://openalex.org/W2964051675","https://openalex.org/W2965197332","https://openalex.org/W2978329087","https://openalex.org/W2978987836","https://openalex.org/W2979300990","https://openalex.org/W2990395122","https://openalex.org/W2990947836","https://openalex.org/W2991221721","https://openalex.org/W2991813857","https://openalex.org/W3007824573","https://openalex.org/W3034359780","https://openalex.org/W3034785721","https://openalex.org/W3035655772","https://openalex.org/W3046428525","https://openalex.org/W3121510528","https://openalex.org/W3143107425","https://openalex.org/W3156457440","https://openalex.org/W3158711590","https://openalex.org/W6745625233","https://openalex.org/W6762466240","https://openalex.org/W6781630272"],"related_works":["https://openalex.org/W806705495","https://openalex.org/W2103835134","https://openalex.org/W2004393819","https://openalex.org/W4214676068","https://openalex.org/W2094752121","https://openalex.org/W1970959459","https://openalex.org/W2357288015","https://openalex.org/W1971182479","https://openalex.org/W1998396719","https://openalex.org/W2979257875"],"abstract_inverted_index":{"The":[0],"goal":[1],"of":[2,43,84,97,142],"Zero-shot":[3],"Learning":[4],"(ZSL)":[5],"is":[6,35],"to":[7,49,64,118,125,130],"discriminate":[8],"images":[9],"from":[10],"unseen":[11],"classes":[12],"by":[13,88],"modelling":[14],"the":[15,31,40,52,66,73,95,120,126,140],"embedding":[16,56],"relationship":[17,57],"between":[18],"visual":[19,44,55,91,101],"and":[20,46,54,90,100],"semantic":[21,33,53,89,99,107,122,128],"features.":[22],"However,":[23],"most":[24],"existing":[25],"methods":[26],"ignore":[27],"a":[28],"fact":[29],"that":[30],"hand-crafted":[32],"feature":[34],"generally":[36],"less":[37],"discriminative":[38],"than":[39],"informative":[41],"knowledge":[42,67],"features":[45,123,129],"therefore":[47],"fail":[48],"effectively":[50],"capture":[51,131],"among":[58,69],"different":[59],"categories.":[60],"In":[61],"this":[62],"brief,":[63],"model":[65],"transfer":[68],"classes,":[70],"we":[71,113],"incorporate":[72],"semantic-visual":[74],"Combination":[75],"Propagation":[76],"Network":[77],"(CPN)":[78],"into":[79],"ZSL.":[80],"Specifically,":[81],"CPN":[82,103],"consists":[83],"two":[85],"graphs":[86],"constructed":[87],"representations":[92,108],"respectively.":[93],"Through":[94],"combination":[96],"abundant":[98],"information,":[102],"can":[104],"learn":[105],"enhanced":[106],"for":[109],"ZSL":[110],"classification.":[111],"Furthermore,":[112],"employ":[114],"an":[115],"auto-encoder":[116],"module":[117],"project":[119],"learned":[121],"back":[124],"original":[127],"complementary":[132],"information.":[133],"Extensive":[134],"experiments":[135],"on":[136],"benchmark":[137],"datasets":[138],"demonstrate":[139],"effectiveness":[141],"CPN.":[143]},"counts_by_year":[{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":7}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
