{"id":"https://openalex.org/W4403022209","doi":"https://doi.org/10.1109/tnnls.2024.3463495","title":"Prototypical Learning Guided Context-Aware Segmentation Network for Few-Shot Anomaly Detection","display_name":"Prototypical Learning Guided Context-Aware Segmentation Network for Few-Shot Anomaly Detection","publication_year":2024,"publication_date":"2024-10-01","ids":{"openalex":"https://openalex.org/W4403022209","doi":"https://doi.org/10.1109/tnnls.2024.3463495","pmid":"https://pubmed.ncbi.nlm.nih.gov/39352823"},"language":"en","primary_location":{"id":"doi:10.1109/tnnls.2024.3463495","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnnls.2024.3463495","pdf_url":null,"source":{"id":"https://openalex.org/S4210175523","display_name":"IEEE Transactions on Neural Networks and Learning Systems","issn_l":"2162-237X","issn":["2162-237X","2162-2388"],"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 Neural Networks and Learning Systems","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"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/A5101815953","display_name":"Yuxin Jiang","orcid":"https://orcid.org/0000-0002-9797-5116"},"institutions":[{"id":"https://openalex.org/I47720641","display_name":"Huazhong University of Science and Technology","ror":"https://ror.org/00p991c53","country_code":"CN","type":"education","lineage":["https://openalex.org/I47720641"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuxin Jiang","raw_affiliation_strings":["State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan, China","institution_ids":["https://openalex.org/I47720641"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044965902","display_name":"Yunkang Cao","orcid":"https://orcid.org/0000-0001-7619-6618"},"institutions":[{"id":"https://openalex.org/I47720641","display_name":"Huazhong University of Science and Technology","ror":"https://ror.org/00p991c53","country_code":"CN","type":"education","lineage":["https://openalex.org/I47720641"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yunkang Cao","raw_affiliation_strings":["State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan, China"],"raw_orcid":"https://orcid.org/0000-0001-7619-6618","affiliations":[{"raw_affiliation_string":"State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan, China","institution_ids":["https://openalex.org/I47720641"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5062049138","display_name":"Weiming Shen","orcid":"https://orcid.org/0000-0001-5204-7992"},"institutions":[{"id":"https://openalex.org/I47720641","display_name":"Huazhong University of Science and Technology","ror":"https://ror.org/00p991c53","country_code":"CN","type":"education","lineage":["https://openalex.org/I47720641"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weiming Shen","raw_affiliation_strings":["State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan, China"],"raw_orcid":"https://orcid.org/0000-0001-5204-7992","affiliations":[{"raw_affiliation_string":"State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan, China","institution_ids":["https://openalex.org/I47720641"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I47720641"],"apc_list":null,"apc_paid":null,"fwci":3.5912,"has_fulltext":false,"cited_by_count":15,"citation_normalized_percentile":{"value":0.9358103,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":"36","issue":"7","first_page":"12016","last_page":"12026"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9980999827384949,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9980999827384949,"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/T12597","display_name":"Fire Detection and Safety Systems","score":0.932200014591217,"subfield":{"id":"https://openalex.org/subfields/2213","display_name":"Safety, Risk, Reliability and Quality"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T13018","display_name":"Seismology and Earthquake Studies","score":0.9136999845504761,"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/anomaly-detection","display_name":"Anomaly detection","score":0.745837926864624},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6712241768836975},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.626252293586731},{"id":"https://openalex.org/keywords/shot","display_name":"Shot (pellet)","score":0.6152986884117126},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6048974990844727},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5386379361152649},{"id":"https://openalex.org/keywords/one-shot","display_name":"One shot","score":0.4372168183326721},{"id":"https://openalex.org/keywords/anomaly","display_name":"Anomaly (physics)","score":0.4346836805343628},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.35911303758621216},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.14124903082847595},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.1310480535030365},{"id":"https://openalex.org/keywords/materials-science","display_name":"Materials science","score":0.0806850790977478},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.06410151720046997}],"concepts":[{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.745837926864624},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6712241768836975},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.626252293586731},{"id":"https://openalex.org/C2778344882","wikidata":"https://www.wikidata.org/wiki/Q278938","display_name":"Shot (pellet)","level":2,"score":0.6152986884117126},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6048974990844727},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5386379361152649},{"id":"https://openalex.org/C2992734406","wikidata":"https://www.wikidata.org/wiki/Q413267","display_name":"One shot","level":2,"score":0.4372168183326721},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.4346836805343628},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.35911303758621216},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.14124903082847595},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.1310480535030365},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.0806850790977478},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.06410151720046997},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C26873012","wikidata":"https://www.wikidata.org/wiki/Q214781","display_name":"Condensed matter physics","level":1,"score":0.0},{"id":"https://openalex.org/C78519656","wikidata":"https://www.wikidata.org/wiki/Q101333","display_name":"Mechanical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C191897082","wikidata":"https://www.wikidata.org/wiki/Q11467","display_name":"Metallurgy","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tnnls.2024.3463495","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnnls.2024.3463495","pdf_url":null,"source":{"id":"https://openalex.org/S4210175523","display_name":"IEEE Transactions on Neural Networks and Learning Systems","issn_l":"2162-237X","issn":["2162-237X","2162-2388"],"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 Neural Networks and Learning Systems","raw_type":"journal-article"},{"id":"pmid:39352823","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/39352823","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE transactions on neural networks and learning systems","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G532376636","display_name":null,"funder_award_id":"HUST: 2021GCRC058","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"}],"funders":[{"id":"https://openalex.org/F4320335787","display_name":"Fundamental Research Funds for the Central Universities","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":47,"referenced_works":["https://openalex.org/W2565639579","https://openalex.org/W2948982773","https://openalex.org/W2964105864","https://openalex.org/W2964137095","https://openalex.org/W2970263181","https://openalex.org/W2990230185","https://openalex.org/W3034314048","https://openalex.org/W3120795858","https://openalex.org/W3159879667","https://openalex.org/W3165716503","https://openalex.org/W3166166117","https://openalex.org/W3169651898","https://openalex.org/W3176065502","https://openalex.org/W3189108671","https://openalex.org/W3200647619","https://openalex.org/W3203211016","https://openalex.org/W3212158549","https://openalex.org/W3217610095","https://openalex.org/W4200434100","https://openalex.org/W4214688034","https://openalex.org/W4224999518","https://openalex.org/W4282940188","https://openalex.org/W4287887190","https://openalex.org/W4289537725","https://openalex.org/W4289792391","https://openalex.org/W4312298392","https://openalex.org/W4312570668","https://openalex.org/W4312762894","https://openalex.org/W4312772600","https://openalex.org/W4313023122","https://openalex.org/W4313291134","https://openalex.org/W4318831100","https://openalex.org/W4377004381","https://openalex.org/W4386813353","https://openalex.org/W4390872785","https://openalex.org/W4391992447","https://openalex.org/W4403095012","https://openalex.org/W4411244487","https://openalex.org/W6684191040","https://openalex.org/W6753441378","https://openalex.org/W6777869702","https://openalex.org/W6785596320","https://openalex.org/W6849441464","https://openalex.org/W6853095873","https://openalex.org/W6853542233","https://openalex.org/W6859192591","https://openalex.org/W6872558144"],"related_works":["https://openalex.org/W2806741695","https://openalex.org/W4290647774","https://openalex.org/W2497720472","https://openalex.org/W3189286258","https://openalex.org/W3207797160","https://openalex.org/W3210364259","https://openalex.org/W4300558037","https://openalex.org/W2912112202","https://openalex.org/W2667207928","https://openalex.org/W4292659306"],"abstract_inverted_index":{"Few-shot":[0],"anomaly":[1,131],"detection":[2,152],"(FSAD)":[3],"denotes":[4],"the":[5,33,59,140,155,168],"identification":[6],"of":[7,17,159],"anomalies":[8,120,135],"within":[9],"a":[10,14,50,77,84,124],"target":[11,41,67],"category":[12],"with":[13,161,193],"limited":[15,194],"number":[16],"normal":[18,101],"samples.":[19,196],"Existing":[20],"FSAD":[21,42,71,157],"methods":[22],"largely":[23],"rely":[24],"on":[25,145,180],"pretrained":[26,38],"feature":[27,64,79,98],"representations":[28,39],"to":[29,57,95,117,138],"detect":[30],"anomalies,":[31],"but":[32],"inherent":[34],"domain":[35,60],"gap":[36],"between":[37],"and":[40,69,83,148,163],"scenarios":[43,68],"is":[44,114,127,199],"often":[45],"overlooked.":[46],"This":[47],"study":[48],"proposes":[49],"prototypical":[51,78,91],"learning-guided":[52],"context-aware":[53,85],"segmentation":[54,86],"network":[55],"(PCSNet)":[56],"address":[58],"gap,":[61],"thereby":[62],"improving":[63],"descriptiveness":[65],"in":[66,173],"enhancing":[70],"performance.":[72],"In":[73],"particular,":[74],"PCSNet":[75,188],"comprises":[76],"adaption":[80],"(PFA)":[81],"subnetwork":[82,126],"(CAS)":[87],"subnetwork.":[88],"PFA":[89],"extracts":[90],"features":[92],"as":[93],"guidance":[94],"ensure":[96],"better":[97],"compactness":[99],"for":[100,129],"data":[102],"while":[103],"distinct":[104],"separation":[105],"from":[106],"anomalies.":[107],"A":[108],"pixel-level":[109,130],"disparity":[110],"classification":[111],"(PDC)":[112],"loss":[113],"also":[115],"designed":[116],"make":[118],"subtle":[119],"more":[121],"distinguishable.":[122],"Then":[123],"CAS":[125],"introduced":[128],"localization,":[132],"where":[133],"pseudo":[134],"are":[136],"exploited":[137],"facilitate":[139],"training":[141,195],"process.":[142],"Experimental":[143],"results":[144,192],"MVTec":[146],"AD":[147],"metal":[149],"part":[150,183],"defect":[151],"(MPDD)":[153],"demonstrate":[154,186],"superior":[156],"performance":[158],"PCSNet,":[160],"94.9%":[162],"80.2%":[164],"image-level":[165],"area":[166],"under":[167],"receiver":[169],"operating":[170],"characteristics":[171],"(AUROCs)":[172],"an":[174],"eight-shot":[175],"scenario,":[176],"respectively.":[177],"Real-world":[178],"applications":[179],"automotive":[181],"plastic":[182],"inspection":[184],"further":[185],"that":[187],"can":[189],"achieve":[190],"promising":[191],"The":[197],"code":[198],"available":[200],"at":[201],"https://github.com/yuxin-jiang/PCSNet.":[202]},"counts_by_year":[{"year":2026,"cited_by_count":8},{"year":2025,"cited_by_count":7}],"updated_date":"2026-08-01T09:00:35.917206","created_date":"2025-10-10T00:00:00"}
