{"id":"https://openalex.org/W4292672491","doi":"https://doi.org/10.1109/tim.2022.3196447","title":"Selective Prototype Network for Few-Shot Metal Surface Defect Segmentation","display_name":"Selective Prototype Network for Few-Shot Metal Surface Defect Segmentation","publication_year":2022,"publication_date":"2022-01-01","ids":{"openalex":"https://openalex.org/W4292672491","doi":"https://doi.org/10.1109/tim.2022.3196447"},"language":"en","primary_location":{"id":"doi:10.1109/tim.2022.3196447","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tim.2022.3196447","pdf_url":null,"source":{"id":"https://openalex.org/S10892749","display_name":"IEEE Transactions on Instrumentation and Measurement","issn_l":"0018-9456","issn":["0018-9456","1557-9662"],"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 Instrumentation and Measurement","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/A5060088885","display_name":"Ruiyun Yu","orcid":"https://orcid.org/0000-0003-0523-6242"},"institutions":[{"id":"https://openalex.org/I9224756","display_name":"Northeastern University","ror":"https://ror.org/03awzbc87","country_code":"CN","type":"education","lineage":["https://openalex.org/I9224756"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ruiyun Yu","raw_affiliation_strings":["Software College, Northeastern University, Shenyang, China"],"raw_orcid":"https://orcid.org/0000-0003-0523-6242","affiliations":[{"raw_affiliation_string":"Software College, Northeastern University, Shenyang, China","institution_ids":["https://openalex.org/I9224756"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5047034942","display_name":"Bingyang Guo","orcid":"https://orcid.org/0000-0002-6630-0044"},"institutions":[{"id":"https://openalex.org/I9224756","display_name":"Northeastern University","ror":"https://ror.org/03awzbc87","country_code":"CN","type":"education","lineage":["https://openalex.org/I9224756"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bingyang Guo","raw_affiliation_strings":["Software College, Northeastern University, Shenyang, China"],"raw_orcid":"https://orcid.org/0000-0002-6630-0044","affiliations":[{"raw_affiliation_string":"Software College, Northeastern University, Shenyang, China","institution_ids":["https://openalex.org/I9224756"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5072066119","display_name":"Kang Yang","orcid":"https://orcid.org/0000-0002-1944-4328"},"institutions":[{"id":"https://openalex.org/I9224756","display_name":"Northeastern University","ror":"https://ror.org/03awzbc87","country_code":"CN","type":"education","lineage":["https://openalex.org/I9224756"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kang Yang","raw_affiliation_strings":["Software College, Northeastern University, Shenyang, China"],"raw_orcid":"https://orcid.org/0000-0002-1944-4328","affiliations":[{"raw_affiliation_string":"Software College, Northeastern University, Shenyang, China","institution_ids":["https://openalex.org/I9224756"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I9224756"],"apc_list":null,"apc_paid":null,"fwci":4.528,"has_fulltext":false,"cited_by_count":42,"citation_normalized_percentile":{"value":0.95142234,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":100},"biblio":{"volume":"71","issue":null,"first_page":"1","last_page":"10"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing Engineering"},"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/T10834","display_name":"Welding Techniques and Residual Stresses","score":0.9957000017166138,"subfield":{"id":"https://openalex.org/subfields/2210","display_name":"Mechanical Engineering"},"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/T13049","display_name":"Surface Roughness and Optical Measurements","score":0.9948999881744385,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6850330829620361},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6831722259521484},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6506044864654541},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.624630331993103},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.562231183052063},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.48536741733551025},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4818669259548187},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.47113698720932007},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4662064015865326}],"concepts":[{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6850330829620361},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6831722259521484},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6506044864654541},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.624630331993103},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.562231183052063},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.48536741733551025},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4818669259548187},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.47113698720932007},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4662064015865326},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","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/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tim.2022.3196447","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tim.2022.3196447","pdf_url":null,"source":{"id":"https://openalex.org/S10892749","display_name":"IEEE Transactions on Instrumentation and Measurement","issn_l":"0018-9456","issn":["0018-9456","1557-9662"],"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 Instrumentation and Measurement","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","score":0.41999998688697815,"id":"https://metadata.un.org/sdg/9"}],"awards":[{"id":"https://openalex.org/G3900590269","display_name":"\u57fa\u4e8e\u6570\u5b57\u5b6a\u751f\u548cAIOT\u7684\u667a\u6167\u6d88\u9632\u5173\u952e\u6280\u672f\u7814\u7a76","funder_award_id":"62072094","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G4737003578","display_name":null,"funder_award_id":"2020JH210100046","funder_id":"https://openalex.org/F4320336742","funder_display_name":"Key Research and Development Program of Liaoning Province"},{"id":"https://openalex.org/G4933828724","display_name":null,"funder_award_id":"XLYC2005001","funder_id":"https://openalex.org/F4320329895","funder_display_name":"Liaoning Revitalization Talents Program"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320329895","display_name":"Liaoning Revitalization Talents Program","ror":null},{"id":"https://openalex.org/F4320336742","display_name":"Key Research and Development Program of Liaoning Province","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":36,"referenced_works":["https://openalex.org/W1677182931","https://openalex.org/W2160551639","https://openalex.org/W2167372553","https://openalex.org/W2194775991","https://openalex.org/W2412782625","https://openalex.org/W2560042709","https://openalex.org/W2563705555","https://openalex.org/W2752782242","https://openalex.org/W2884585870","https://openalex.org/W2901457990","https://openalex.org/W2904559969","https://openalex.org/W2955058313","https://openalex.org/W2963078159","https://openalex.org/W2963599420","https://openalex.org/W2964051675","https://openalex.org/W2982220924","https://openalex.org/W2983850069","https://openalex.org/W2990230185","https://openalex.org/W2994615081","https://openalex.org/W3025177399","https://openalex.org/W3034552520","https://openalex.org/W3106583357","https://openalex.org/W3106906018","https://openalex.org/W3108189450","https://openalex.org/W3113270537","https://openalex.org/W3164289800","https://openalex.org/W3167559252","https://openalex.org/W3169024950","https://openalex.org/W3171297660","https://openalex.org/W3176065502","https://openalex.org/W4254197176","https://openalex.org/W6637373629","https://openalex.org/W6753038380","https://openalex.org/W6761855798","https://openalex.org/W6768021236","https://openalex.org/W6781222066"],"related_works":["https://openalex.org/W3135697610","https://openalex.org/W2085033728","https://openalex.org/W4285411112","https://openalex.org/W2171299904","https://openalex.org/W1647606319","https://openalex.org/W2922442631","https://openalex.org/W4390494008","https://openalex.org/W2053596378","https://openalex.org/W2168523118","https://openalex.org/W1522196789"],"abstract_inverted_index":{"Metal":[0],"Surface":[1],"defects":[2,13,142,163],"segmentation":[3,28,203],"is":[4,210],"a":[5,81,102,114,120,174,205],"critical":[6],"task":[7],"to":[8,42,73,100,108,128,191,200],"make":[9],"pixel-level":[10],"predictions":[11],"about":[12],"in":[14,23],"the":[15,47,54,75,125,135,138,161,187,202,215],"industrial":[16],"production":[17],"process,":[18],"which":[19,98],"has":[20,50],"great":[21],"significance":[22],"improving":[24],"product":[25],"quality.":[26],"Existing":[27],"algorithms":[29],"use":[30],"numerous":[31],"labeled":[32,116],"defective":[33,62],"images":[34,63],"for":[35,92,148],"training":[36],"and":[37,53,64,143,164],"can":[38,105],"not":[39],"be":[40,106],"generalized":[41,107],"different":[43,51,149],"metal":[44,48,94,140,150],"surfaces.":[45],"Additionally,":[46],"surface":[49,95,110,141,151],"materials":[52],"defect":[55,96,117],"samples":[56],"are":[57],"insufficient.":[58],"That":[59],"means":[60],"collecting":[61],"annotates":[65],"pixel":[66],"labels":[67],"takes":[68],"more":[69,193],"time.":[70],"In":[71,198],"order":[72],"solve":[74],"above":[76],"problems,":[77],"this":[78],"paper":[79],"proposed":[80],"novel":[82,109,183],"selective":[83,121],"prototype":[84,122],"network":[85],"(SPNet)":[86],"with":[87,112],"matrix":[88,180],"decomposition":[89],"attention":[90,176,184],"mechanism":[91,177],"few-shot":[93],"segmentation,":[97],"aims":[99],"learn":[101,129],"model":[103],"that":[104,219],"classes":[111],"only":[113],"few":[115],"samples.":[118],"Using":[119],"acquired":[123],"from":[124,160],"support":[126],"image":[127],"query":[130],"image,":[131],"SPNet":[132,155,172,220],"efficiently":[133],"utilizes":[134,157],"information":[136],"of":[137],"same":[139],"meanwhile":[144],"offers":[145],"sufficient":[146],"representation":[147,190],"defects.":[152,170],"With":[153],"this,":[154],"fully":[156],"correlation":[158],"knowledge":[159],"known":[162],"provides":[165],"better":[166],"generalization":[167],"on":[168,179,214],"unknown":[169],"Moreover,":[171],"introduces":[173],"feature":[175,189],"based":[178],"decomposition.":[181],"The":[182],"method":[185],"factorizes":[186],"complicated":[188],"acquire":[192],"accurate":[194],"global":[195],"context":[196],"information.":[197],"addition,":[199],"improve":[201],"performance,":[204],"conditional":[206],"boundary":[207],"refinement":[208],"module":[209],"proposed.":[211],"Experimental":[212],"results":[213],"Defects":[216],"dataset":[217],"show":[218],"achieves":[221],"state-of-the-art":[222],"performance.":[223]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":13},{"year":2024,"cited_by_count":17},{"year":2023,"cited_by_count":9},{"year":2022,"cited_by_count":1}],"updated_date":"2026-08-01T09:00:35.917206","created_date":"2025-10-10T00:00:00"}
