{"id":"https://openalex.org/W3089390372","doi":"https://doi.org/10.3233/faia200128","title":"A Region Selection Model to Identify Unknown Unknowns in Image Datasets","display_name":"A Region Selection Model to Identify Unknown Unknowns in Image Datasets","publication_year":2020,"publication_date":"2020-01-01","ids":{"openalex":"https://openalex.org/W3089390372","doi":"https://doi.org/10.3233/faia200128","mag":"3089390372"},"language":"en","primary_location":{"id":"doi:10.3233/faia200128","is_oa":true,"landing_page_url":"https://doi.org/10.3233/faia200128","pdf_url":null,"source":{"id":"https://openalex.org/S4210201731","display_name":"Frontiers in artificial intelligence and applications","issn_l":"0922-6389","issn":["0922-6389","1879-8314"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Artificial Intelligence and Applications","raw_type":"book-chapter"},"type":"book-chapter","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://doi.org/10.3233/faia200128","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101961088","display_name":"Xiao Dong","orcid":"https://orcid.org/0000-0001-9519-612X"},"institutions":[{"id":"https://openalex.org/I165143802","display_name":"The University of Queensland","ror":"https://ror.org/00rqy9422","country_code":"AU","type":"education","lineage":["https://openalex.org/I165143802"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Dong Xiao","raw_affiliation_strings":["University of Queensland, Brisbane, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Queensland, Brisbane, Australia","institution_ids":["https://openalex.org/I165143802"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Zhang Huaxiang","orcid":null},"institutions":[{"id":"https://openalex.org/I28006308","display_name":"Shandong Normal University","ror":"https://ror.org/01wy3h363","country_code":"CN","type":"education","lineage":["https://openalex.org/I28006308"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhang Huaxiang","raw_affiliation_strings":["Shandong Normal University, Jinan, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shandong Normal University, Jinan, China","institution_ids":["https://openalex.org/I28006308"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5052565959","display_name":"Gianluca Demartini","orcid":"https://orcid.org/0000-0002-7311-3693"},"institutions":[{"id":"https://openalex.org/I165143802","display_name":"The University of Queensland","ror":"https://ror.org/00rqy9422","country_code":"AU","type":"education","lineage":["https://openalex.org/I165143802"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Demartini Gianluca","raw_affiliation_strings":["University of Queensland, Brisbane, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Queensland, Brisbane, Australia","institution_ids":["https://openalex.org/I165143802"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.5362,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.67404473,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":93},"biblio":{"volume":"325","issue":null,"first_page":"474","last_page":"481"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9947999715805054,"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"}},"topics":[{"id":"https://openalex.org/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9947999715805054,"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/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.9947999715805054,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9915000200271606,"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/selection","display_name":"Selection (genetic algorithm)","score":0.6575899720191956},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.5844215154647827},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5254672765731812},{"id":"https://openalex.org/keywords/model-selection","display_name":"Model selection","score":0.4851246774196625},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4844265282154083},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.47367507219314575},{"id":"https://openalex.org/keywords/computational-biology","display_name":"Computational biology","score":0.3282444477081299},{"id":"https://openalex.org/keywords/biology","display_name":"Biology","score":0.16872525215148926}],"concepts":[{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.6575899720191956},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.5844215154647827},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5254672765731812},{"id":"https://openalex.org/C93959086","wikidata":"https://www.wikidata.org/wiki/Q6888345","display_name":"Model selection","level":2,"score":0.4851246774196625},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4844265282154083},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.47367507219314575},{"id":"https://openalex.org/C70721500","wikidata":"https://www.wikidata.org/wiki/Q177005","display_name":"Computational biology","level":1,"score":0.3282444477081299},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.16872525215148926}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.3233/faia200128","is_oa":true,"landing_page_url":"https://doi.org/10.3233/faia200128","pdf_url":null,"source":{"id":"https://openalex.org/S4210201731","display_name":"Frontiers in artificial intelligence and applications","issn_l":"0922-6389","issn":["0922-6389","1879-8314"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Artificial Intelligence and Applications","raw_type":"book-chapter"},{"id":"mag:3089390372","is_oa":false,"landing_page_url":"https://dblp.uni-trier.de/db/conf/ecai/ecai2020.html#Dong0D20","pdf_url":null,"source":{"id":"https://openalex.org/S4306418308","display_name":"European Conference on Artificial Intelligence","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":"European Conference on Artificial Intelligence","raw_type":null}],"best_oa_location":{"id":"doi:10.3233/faia200128","is_oa":true,"landing_page_url":"https://doi.org/10.3233/faia200128","pdf_url":null,"source":{"id":"https://openalex.org/S4210201731","display_name":"Frontiers in artificial intelligence and applications","issn_l":"0922-6389","issn":["0922-6389","1879-8314"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Artificial Intelligence and Applications","raw_type":"book-chapter"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":9,"referenced_works":["https://openalex.org/W1686810756","https://openalex.org/W2059362837","https://openalex.org/W2085989833","https://openalex.org/W2097726431","https://openalex.org/W2128678390","https://openalex.org/W2583689529","https://openalex.org/W2742330194","https://openalex.org/W2788428929","https://openalex.org/W2963446712"],"related_works":["https://openalex.org/W4205762803","https://openalex.org/W2535856026","https://openalex.org/W2265065644","https://openalex.org/W2134699697","https://openalex.org/W3017188156","https://openalex.org/W2322875716","https://openalex.org/W2383516975","https://openalex.org/W2374878784","https://openalex.org/W2147679489","https://openalex.org/W2371642785"],"abstract_inverted_index":{"Unknown":[0],"Unknowns":[1],"(UUs)":[2],"are":[3,133],"a":[4,45,67,75,113,145],"kind":[5],"of":[6,33,102,160],"test":[7,52],"data":[8,95],"points":[9],"on":[10,153],"which":[11],"predictive":[12,34],"model":[13,47],"confidence":[14],"is":[15,22,106],"high":[16,59,100],"but":[17],"the":[18,31,94,120,157,161],"prediction":[19],"incorrect.":[20],"It":[21],"crucial":[23],"to":[24,37,48,81,135,173],"identify":[25,49,82,89],"these":[26,63],"instances":[27],"for":[28],"better":[29],"understanding":[30],"limitation":[32],"models":[35],"and":[36,58,79,116,130,170],"avoid":[38],"critical":[39],"errors.":[40],"However,":[41],"existing":[42],"methods":[43],"utilize":[44],"fixed":[46],"UUs":[50,83,98],"in":[51,55,84,93],"data,":[53],"resulting":[54],"limited":[56],"performance":[57,159],"cost.":[60],"To":[61],"address":[62],"limitations,":[64],"we":[65,88],"propose":[66],"regional":[68],"candidate":[69,91],"selection":[70],"algorithm":[71],"that":[72],"seamlessly":[73],"combines":[74],"deep":[76,114],"neural":[77],"network":[78,115],"humans":[80],"visual":[85],"data.":[86],"Specifically,":[87],"several":[90],"regions":[92],"space":[96],"where":[97],"have":[99],"probability":[101],"being":[103],"present.":[104],"This":[105],"achieved":[107],"by":[108,112,126],"comparing":[109],"labels":[110],"learned":[111],"predictions":[117],"obtained":[118],"with":[119],"original":[121],"classification":[122],"model.":[123],"Moreover,":[124],"inspired":[125],"active":[127],"learning,":[128],"diversity":[129],"training":[131],"loss":[132],"utilized":[134],"obtain":[136],"suitable":[137],"query":[138],"sequences.":[139],"We":[140],"evaluate":[141],"our":[142],"method":[143,163],"using":[144],"publicly":[146],"available":[147],"image":[148],"dataset.":[149],"Experimental":[150],"results":[151],"conducted":[152],"this":[154],"dataset":[155],"demonstrate":[156],"improved":[158],"proposed":[162],"over":[164],"different":[165,168],"baselines":[166],"under":[167],"conditions":[169],"its":[171],"robustness":[172],"noisy":[174],"labels.":[175]},"counts_by_year":[{"year":2021,"cited_by_count":1}],"updated_date":"2026-07-15T18:14:33.161393","created_date":"2025-10-10T00:00:00"}