{"id":"https://openalex.org/W3190960741","doi":"https://doi.org/10.1145/3474085.3475232","title":"Few-shot Unsupervised Domain Adaptation with Image-to-Class Sparse Similarity Encoding","display_name":"Few-shot Unsupervised Domain Adaptation with Image-to-Class Sparse Similarity Encoding","publication_year":2021,"publication_date":"2021-10-17","ids":{"openalex":"https://openalex.org/W3190960741","doi":"https://doi.org/10.1145/3474085.3475232","mag":"3190960741"},"language":"en","primary_location":{"id":"doi:10.1145/3474085.3475232","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3474085.3475232","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 29th ACM International Conference on Multimedia","raw_type":"proceedings-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2108.02953","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5075443756","display_name":"Shengqi Huang","orcid":null},"institutions":[{"id":"https://openalex.org/I152031979","display_name":"Nanjing Normal University","ror":"https://ror.org/036trcv74","country_code":"CN","type":"education","lineage":["https://openalex.org/I152031979"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Shengqi Huang","raw_affiliation_strings":["Nanjing Normal University, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"Nanjing Normal University, Nanjing, China","institution_ids":["https://openalex.org/I152031979"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5078658353","display_name":"Wanqi Yang","orcid":"https://orcid.org/0000-0001-6727-6077"},"institutions":[{"id":"https://openalex.org/I152031979","display_name":"Nanjing Normal University","ror":"https://ror.org/036trcv74","country_code":"CN","type":"education","lineage":["https://openalex.org/I152031979"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wanqi Yang","raw_affiliation_strings":["Nanjing Normal University, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"Nanjing Normal University, Nanjing, China","institution_ids":["https://openalex.org/I152031979"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100435848","display_name":"Lei Wang","orcid":"https://orcid.org/0000-0002-0961-0441"},"institutions":[{"id":"https://openalex.org/I204824540","display_name":"University of Wollongong","ror":"https://ror.org/00jtmb277","country_code":"AU","type":"education","lineage":["https://openalex.org/I204824540"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Lei Wang","raw_affiliation_strings":["University of Wollongong, Wollongong, Australia"],"affiliations":[{"raw_affiliation_string":"University of Wollongong, Wollongong, Australia","institution_ids":["https://openalex.org/I204824540"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100643784","display_name":"Luping Zhou","orcid":"https://orcid.org/0000-0001-8762-2424"},"institutions":[{"id":"https://openalex.org/I129604602","display_name":"University of Sydney","ror":"https://ror.org/0384j8v12","country_code":"AU","type":"education","lineage":["https://openalex.org/I129604602"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Luping Zhou","raw_affiliation_strings":["University of Sydney, Sydney, Australia"],"affiliations":[{"raw_affiliation_string":"University of Sydney, Sydney, Australia","institution_ids":["https://openalex.org/I129604602"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101400128","display_name":"Ming Yang","orcid":"https://orcid.org/0000-0001-8936-4270"},"institutions":[{"id":"https://openalex.org/I152031979","display_name":"Nanjing Normal University","ror":"https://ror.org/036trcv74","country_code":"CN","type":"education","lineage":["https://openalex.org/I152031979"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ming Yang","raw_affiliation_strings":["Nanjing Normal University, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"Nanjing Normal University, Nanjing, China","institution_ids":["https://openalex.org/I152031979"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5075443756"],"corresponding_institution_ids":["https://openalex.org/I152031979"],"apc_list":null,"apc_paid":null,"fwci":0.6999,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.76215318,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"677","last_page":"685"},"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9714999794960022,"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":0.8033831119537354},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7343024611473083},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6899988055229187},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6686442494392395},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.627453088760376},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.6152517795562744},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5419962406158447},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5206239223480225},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.4993875026702881},{"id":"https://openalex.org/keywords/encoding","display_name":"Encoding (memory)","score":0.4943230152130127},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.450992614030838},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.44020697474479675},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.41851726174354553},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.41755443811416626},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3655356764793396},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.34546399116516113},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1553685963153839}],"concepts":[{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.8033831119537354},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7343024611473083},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6899988055229187},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6686442494392395},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.627453088760376},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.6152517795562744},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5419962406158447},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5206239223480225},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4993875026702881},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.4943230152130127},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.450992614030838},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.44020697474479675},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.41851726174354553},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.41755443811416626},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3655356764793396},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.34546399116516113},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1553685963153839},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"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/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","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}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1145/3474085.3475232","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3474085.3475232","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 29th ACM International Conference on Multimedia","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2108.02953","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2108.02953","pdf_url":"https://arxiv.org/pdf/2108.02953","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"pmh:oai:ro.uow.edu.au:test2021-4621","is_oa":false,"landing_page_url":"https://ro.uow.edu.au/test2021/3612","pdf_url":null,"source":{"id":"https://openalex.org/S4306400510","display_name":"Research Online (University of Wollongong)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I204824540","host_organization_name":"University of Wollongong","host_organization_lineage":["https://openalex.org/I204824540"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Test Series for Scopus Harvesting 2021","raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2108.02953","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2108.02953","pdf_url":"https://arxiv.org/pdf/2108.02953","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.6499999761581421}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":46,"referenced_works":["https://openalex.org/W1565327149","https://openalex.org/W1731081199","https://openalex.org/W2159291411","https://openalex.org/W2593768305","https://openalex.org/W2601450892","https://openalex.org/W2604763608","https://openalex.org/W2605195953","https://openalex.org/W2753160622","https://openalex.org/W2767699072","https://openalex.org/W2788305286","https://openalex.org/W2904218366","https://openalex.org/W2921509486","https://openalex.org/W2947124992","https://openalex.org/W2951670162","https://openalex.org/W2951775809","https://openalex.org/W2962687275","https://openalex.org/W2962723986","https://openalex.org/W2962987395","https://openalex.org/W2963233928","https://openalex.org/W2963327260","https://openalex.org/W2963341924","https://openalex.org/W2963741406","https://openalex.org/W2963809521","https://openalex.org/W2963890275","https://openalex.org/W2963943197","https://openalex.org/W2964206659","https://openalex.org/W2971035910","https://openalex.org/W2979689312","https://openalex.org/W2980347982","https://openalex.org/W2981720610","https://openalex.org/W2996623013","https://openalex.org/W2996787432","https://openalex.org/W2998115938","https://openalex.org/W3001411605","https://openalex.org/W3005304986","https://openalex.org/W3012209922","https://openalex.org/W3012255272","https://openalex.org/W3015217610","https://openalex.org/W3034187513","https://openalex.org/W3034312118","https://openalex.org/W3034324233","https://openalex.org/W3035009331","https://openalex.org/W3035519852","https://openalex.org/W4288326400","https://openalex.org/W4295274059","https://openalex.org/W4306979394"],"related_works":["https://openalex.org/W2965546495","https://openalex.org/W4389116644","https://openalex.org/W2153315159","https://openalex.org/W3103844505","https://openalex.org/W259157601","https://openalex.org/W4205463238","https://openalex.org/W2110523656","https://openalex.org/W2761785940","https://openalex.org/W2129933262","https://openalex.org/W2565656575"],"abstract_inverted_index":{"This":[0],"paper":[1],"investigates":[2],"a":[3,49,130,171,219],"valuable":[4],"setting":[5,234],"called":[6,133],"few-shot":[7,32,61,108],"unsupervised":[8],"domain":[9,26,38,68,126,166,200,203],"adaptation":[10,69],"(FS-UDA),":[11],"which":[12],"has":[13],"not":[14,139],"been":[15],"sufficiently":[16],"studied":[17],"in":[18,79,151],"the":[19,24,36,44,55,60,67,95,103,143,158,164,184,193,197,225,232],"literature.":[20],"In":[21,237],"this":[22],"setting,":[23,46],"source":[25,97],"data":[27,39,63],"are":[28,40,211],"labelled,":[29],"but":[30,155],"with":[31],"per":[33,64],"category,":[34],"while":[35],"target":[37,99],"unlabelled.":[41],"To":[42],"address":[43],"FS-UDA":[45,92],"we":[47,169],"develop":[48],"general":[50,78,112],"UDA":[51,113],"model":[52,76,114,138],"to":[53,87,90,182],"solve":[54],"following":[56],"two":[57],"key":[58],"issues:":[59],"labeled":[62],"category":[65],"and":[66,72,98,125,190,206],"between":[70],"support":[71],"query":[73],"sets.":[74],"Our":[75],"is":[77,115],"that":[80,148],"once":[81],"trained":[82],"it":[83],"will":[84],"be":[85,88],"able":[86],"applied":[89],"various":[91],"tasks":[93],"from":[94],"same":[96],"domains.":[100],"Inspired":[101],"by":[102],"recent":[104,251],"local":[105,119,185,208],"descriptor":[106],"based":[107],"learning":[109],"(FSL),":[110],"our":[111,137,229,241],"fully":[116],"built":[117],"upon":[118,213],"descriptors":[120],"(LDs)":[121],"for":[122,188,199,231,239],"image":[123,160],"classification":[124,189],"adaptation.":[127,201],"By":[128],"proposing":[129],"novel":[131,172,233],"concept":[132],"similarity":[134,161],"patterns":[135],"(SPs),":[136],"only":[140],"effectively":[141],"considers":[142],"spatial":[144],"relationship":[145],"of":[146,196,228,235,250],"LDs":[147],"was":[149],"ignored":[150],"previous":[152],"FSL":[153,252],"methods,":[154],"also":[156,244],"makes":[157],"learned":[159],"better":[162,246],"serve":[163],"required":[165],"alignment.":[167],"Specifically,":[168],"propose":[170],"IMage-to-class":[173],"sparse":[174],"Similarity":[175],"Encoding":[176],"(IMSE)":[177],"method.":[178],"It":[179],"learns":[180],"SPs":[181,198],"extract":[183],"discriminative":[186],"information":[187],"meanwhile":[191],"aligns":[192],"covariance":[194],"matrix":[195],"Also,":[202],"adversarial":[204],"training":[205],"multi-scale":[207],"feature":[209],"matching":[210],"performed":[212],"LDs.":[214],"Extensive":[215],"experiments":[216],"conducted":[217],"on":[218,254],"multi-domain":[220],"benchmark":[221],"dataset":[222],"DomainNet":[223],"demonstrates":[224],"state-of-the-art":[226],"performance":[227,247],"IMSE":[230,242],"FS-UDA.":[236],"addition,":[238],"FSL,":[240],"can":[243],"show":[245],"than":[248],"most":[249],"methods":[253],"miniImageNet.":[255]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":2}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2025-10-10T00:00:00"}
