{"id":"https://openalex.org/W2965973206","doi":"https://doi.org/10.24963/ijcai.2019/555","title":"Zero-shot Metric Learning","display_name":"Zero-shot Metric Learning","publication_year":2019,"publication_date":"2019-07-28","ids":{"openalex":"https://openalex.org/W2965973206","doi":"https://doi.org/10.24963/ijcai.2019/555","mag":"2965973206"},"language":"en","primary_location":{"id":"doi:10.24963/ijcai.2019/555","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2019/555","pdf_url":"https://www.ijcai.org/proceedings/2019/0555.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":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.ijcai.org/proceedings/2019/0555.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101694146","display_name":"Xinyi Xu","orcid":"https://orcid.org/0000-0002-4776-2809"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xinyi Xu","raw_affiliation_strings":["School of Electronic Engineering, Xidian University, Xian 710071, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electronic Engineering, Xidian University, Xian 710071, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5059938102","display_name":"Huanhuan Cao","orcid":"https://orcid.org/0009-0007-8273-5439"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Huanhuan Cao","raw_affiliation_strings":["School of Electronic Engineering, Xidian University, Xian 710071, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electronic Engineering, Xidian University, Xian 710071, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5035747364","display_name":"Yanhua Yang","orcid":"https://orcid.org/0000-0002-7916-3683"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yanhua Yang","raw_affiliation_strings":["School of Electronic Engineering, Xidian University, Xian 710071, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electronic Engineering, Xidian University, Xian 710071, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5057526429","display_name":"Erkun Yang","orcid":"https://orcid.org/0000-0002-0855-1646"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Erkun Yang","raw_affiliation_strings":["School of Electronic Engineering, Xidian University, Xian 710071, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electronic Engineering, Xidian University, Xian 710071, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5015874725","display_name":"Cheng Deng","orcid":"https://orcid.org/0000-0003-2620-3247"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Cheng Deng","raw_affiliation_strings":["School of Electronic Engineering, Xidian University, Xian 710071, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electronic Engineering, Xidian University, Xian 710071, China","institution_ids":["https://openalex.org/I149594827"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I149594827"],"apc_list":null,"apc_paid":null,"fwci":0.7654,"has_fulltext":false,"cited_by_count":12,"citation_normalized_percentile":{"value":0.77665748,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"3996","last_page":"4002"},"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/T11775","display_name":"COVID-19 diagnosis using AI","score":0.995199978351593,"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":0.9945999979972839,"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/metric","display_name":"Metric (unit)","score":0.7358255386352539},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6999378800392151},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6911697387695312},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.6665385365486145},{"id":"https://openalex.org/keywords/relation","display_name":"Relation (database)","score":0.6608145833015442},{"id":"https://openalex.org/keywords/transferability","display_name":"Transferability","score":0.5956825017929077},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5304887890815735},{"id":"https://openalex.org/keywords/transfer-of-learning","display_name":"Transfer of learning","score":0.46919509768486023},{"id":"https://openalex.org/keywords/nearest-neighbor-search","display_name":"Nearest neighbor search","score":0.4649703800678253},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.45428183674812317},{"id":"https://openalex.org/keywords/zero","display_name":"Zero (linguistics)","score":0.4141315817832947},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.37743622064590454},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.334366112947464},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.12593933939933777},{"id":"https://openalex.org/keywords/logit","display_name":"Logit","score":0.06466677784919739}],"concepts":[{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.7358255386352539},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6999378800392151},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6911697387695312},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.6665385365486145},{"id":"https://openalex.org/C25343380","wikidata":"https://www.wikidata.org/wiki/Q277521","display_name":"Relation (database)","level":2,"score":0.6608145833015442},{"id":"https://openalex.org/C61272859","wikidata":"https://www.wikidata.org/wiki/Q7834031","display_name":"Transferability","level":3,"score":0.5956825017929077},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5304887890815735},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.46919509768486023},{"id":"https://openalex.org/C116738811","wikidata":"https://www.wikidata.org/wiki/Q608751","display_name":"Nearest neighbor search","level":2,"score":0.4649703800678253},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.45428183674812317},{"id":"https://openalex.org/C2780813799","wikidata":"https://www.wikidata.org/wiki/Q3274237","display_name":"Zero (linguistics)","level":2,"score":0.4141315817832947},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.37743622064590454},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.334366112947464},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.12593933939933777},{"id":"https://openalex.org/C140331021","wikidata":"https://www.wikidata.org/wiki/Q1868104","display_name":"Logit","level":2,"score":0.06466677784919739},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"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/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.24963/ijcai.2019/555","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2019/555","pdf_url":"https://www.ijcai.org/proceedings/2019/0555.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.2019/555","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2019/555","pdf_url":"https://www.ijcai.org/proceedings/2019/0555.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/G1461465359","display_name":null,"funder_award_id":"61703327","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6777872865","display_name":null,"funder_award_id":"61572388","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G744862195","display_name":null,"funder_award_id":"61572388 and 61703327","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"},{"id":"https://openalex.org/F4320329783","display_name":"Key Industry Innovation Chain of Shaanxi","ror":null}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2965973206.pdf","grobid_xml":"https://content.openalex.org/works/W2965973206.grobid-xml"},"referenced_works_count":31,"referenced_works":["https://openalex.org/W1797268635","https://openalex.org/W1954735160","https://openalex.org/W1996309403","https://openalex.org/W2005118650","https://openalex.org/W2015563892","https://openalex.org/W2106053110","https://openalex.org/W2106097867","https://openalex.org/W2108598243","https://openalex.org/W2109824782","https://openalex.org/W2112796928","https://openalex.org/W2113325037","https://openalex.org/W2116339064","https://openalex.org/W2117154949","https://openalex.org/W2128532956","https://openalex.org/W2130556178","https://openalex.org/W2137736727","https://openalex.org/W2138621090","https://openalex.org/W2154455818","https://openalex.org/W2155541015","https://openalex.org/W2155893237","https://openalex.org/W2167686991","https://openalex.org/W2169495281","https://openalex.org/W2475245514","https://openalex.org/W2543665857","https://openalex.org/W2549607029","https://openalex.org/W2950094539","https://openalex.org/W2950153207","https://openalex.org/W2963026686","https://openalex.org/W4239072543","https://openalex.org/W4294338724","https://openalex.org/W4294375521"],"related_works":["https://openalex.org/W4399895933","https://openalex.org/W2161221533","https://openalex.org/W4229699405","https://openalex.org/W1666484574","https://openalex.org/W2216382288","https://openalex.org/W2355491300","https://openalex.org/W4234629551","https://openalex.org/W2011110943","https://openalex.org/W2028856635","https://openalex.org/W4251615416"],"abstract_inverted_index":{"In":[0],"this":[1],"work,":[2],"we":[3,76,95],"tackle":[4],"the":[5,19,28],"zero-shot":[6],"metric":[7,25],"learning":[8],"problem":[9],"and":[10,63,89,111],"propose":[11],"a":[12,23,79,97],"novel":[13],"method":[14],"abbreviated":[15],"as":[16],"ZSML,":[17],"with":[18],"purpose":[20],"to":[21,70,85,102],"learn":[22],"distance":[24],"that":[26,119],"measures":[27],"similarity":[29],"of":[30,81],"unseen":[31,34],"categories":[32],"(even":[33],"datasets).":[35],"ZSML":[36,120],"achieves":[37],"strong":[38],"transferability":[39],"by":[40,51],"capturing":[41],"multi-nonlinear":[42],"yet":[43],"continuous":[44,72,104],"relation":[45,92],"among":[46],"data.":[47],"It":[48],"is":[49,68],"motivated":[50],"two":[52],"facts:":[53],"1)":[54],"relations":[55],"can":[56,121],"be":[57],"essentially":[58],"described":[59],"from":[60],"various":[61],"perspectives;":[62],"2)":[64],"traditional":[65],"binary":[66],"supervision":[67],"insufficient":[69],"represent":[71],"visual":[73],"similarity.":[74,105],"Specifically,":[75],"first":[77],"reformulate":[78],"collection":[80],"specific-shaped":[82],"convolutional":[83],"kernels":[84],"combine":[86],"data":[87],"pairs":[88],"generate":[90],"multiple":[91],"vectors.":[93],"Furthermore,":[94],"design":[96],"new":[98],"cross-update":[99],"regression":[100],"loss":[101],"discover":[103],"Extensive":[106],"experiments":[107],"including":[108],"intra-dataset":[109],"transfer":[110,113],"inter-dataset":[112],"on":[114],"four":[115],"benchmark":[116],"datasets":[117],"demonstrate":[118],"achieve":[122],"state-of-the-art":[123],"performance.":[124]},"counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":5}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
