{"id":"https://openalex.org/W7127282509","doi":"https://doi.org/10.1109/access.2026.3660103","title":"CLIP-Core: A Few-Sample Anomaly Detection Method for Surface Defects","display_name":"CLIP-Core: A Few-Sample Anomaly Detection Method for Surface Defects","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7127282509","doi":"https://doi.org/10.1109/access.2026.3660103"},"language":null,"primary_location":{"id":"doi:10.1109/access.2026.3660103","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3660103","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1109/access.2026.3660103","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Liang Xu","orcid":"https://orcid.org/0000-0001-5671-4510"},"institutions":[{"id":"https://openalex.org/I139024713","display_name":"Guangdong University of Technology","ror":"https://ror.org/04azbjn80","country_code":"CN","type":"education","lineage":["https://openalex.org/I139024713"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Liang Xu","raw_affiliation_strings":["School of Automation, Guangdong University of Technology, Guangzhou, Guangdong, China"],"raw_orcid":"https://orcid.org/0000-0001-5671-4510","affiliations":[{"raw_affiliation_string":"School of Automation, Guangdong University of Technology, Guangzhou, Guangdong, China","institution_ids":["https://openalex.org/I139024713"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Junle Rao","orcid":"https://orcid.org/0009-0002-0788-7604"},"institutions":[{"id":"https://openalex.org/I139024713","display_name":"Guangdong University of Technology","ror":"https://ror.org/04azbjn80","country_code":"CN","type":"education","lineage":["https://openalex.org/I139024713"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Junle Rao","raw_affiliation_strings":["School of Automation, Guangdong University of Technology, Guangzhou, Guangdong, China"],"raw_orcid":"https://orcid.org/0009-0002-0788-7604","affiliations":[{"raw_affiliation_string":"School of Automation, Guangdong University of Technology, Guangzhou, Guangdong, China","institution_ids":["https://openalex.org/I139024713"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5124948513","display_name":"Shuyou Lin","orcid":null},"institutions":[{"id":"https://openalex.org/I139024713","display_name":"Guangdong University of Technology","ror":"https://ror.org/04azbjn80","country_code":"CN","type":"education","lineage":["https://openalex.org/I139024713"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shuyou Lin","raw_affiliation_strings":["School of Automation, Guangdong University of Technology, Guangzhou, Guangdong, China"],"raw_orcid":"https://orcid.org/0009-0004-4880-8858","affiliations":[{"raw_affiliation_string":"School of Automation, Guangdong University of Technology, Guangzhou, Guangdong, China","institution_ids":["https://openalex.org/I139024713"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I139024713"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.21909536,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"14","issue":null,"first_page":"20716","last_page":"20733"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.76419997215271,"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":0.76419997215271,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.0414000004529953,"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":0.025800000876188278,"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":0.7376999855041504},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.5723000168800354},{"id":"https://openalex.org/keywords/mahalanobis-distance","display_name":"Mahalanobis distance","score":0.5702999830245972},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5579000115394592},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5547000169754028},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5149999856948853},{"id":"https://openalex.org/keywords/histogram","display_name":"Histogram","score":0.43220001459121704},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.41519999504089355},{"id":"https://openalex.org/keywords/invariant","display_name":"Invariant (physics)","score":0.414000004529953}],"concepts":[{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.7376999855041504},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6780999898910522},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.590399980545044},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.5723000168800354},{"id":"https://openalex.org/C1921717","wikidata":"https://www.wikidata.org/wiki/Q1334846","display_name":"Mahalanobis distance","level":2,"score":0.5702999830245972},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5579000115394592},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5547000169754028},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5149999856948853},{"id":"https://openalex.org/C53533937","wikidata":"https://www.wikidata.org/wiki/Q185020","display_name":"Histogram","level":3,"score":0.43220001459121704},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.41519999504089355},{"id":"https://openalex.org/C190470478","wikidata":"https://www.wikidata.org/wiki/Q2370229","display_name":"Invariant (physics)","level":2,"score":0.414000004529953},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.3917999863624573},{"id":"https://openalex.org/C58471807","wikidata":"https://www.wikidata.org/wiki/Q327120","display_name":"Receiver operating characteristic","level":2,"score":0.3700000047683716},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3479999899864197},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.34529998898506165},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.3449000120162964},{"id":"https://openalex.org/C206588197","wikidata":"https://www.wikidata.org/wiki/Q846574","display_name":"Reuse","level":2,"score":0.33869999647140503},{"id":"https://openalex.org/C115901376","wikidata":"https://www.wikidata.org/wiki/Q184199","display_name":"Automation","level":2,"score":0.33660000562667847},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.3165000081062317},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.2953999936580658},{"id":"https://openalex.org/C32022120","wikidata":"https://www.wikidata.org/wiki/Q797225","display_name":"Interference (communication)","level":3,"score":0.29010000824928284},{"id":"https://openalex.org/C32236832","wikidata":"https://www.wikidata.org/wiki/Q80228","display_name":"Bottle","level":2,"score":0.28610000014305115},{"id":"https://openalex.org/C2776836416","wikidata":"https://www.wikidata.org/wiki/Q1364844","display_name":"False alarm","level":2,"score":0.28540000319480896},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.2761000096797943},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2720000147819519},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.2709999978542328},{"id":"https://openalex.org/C152745839","wikidata":"https://www.wikidata.org/wiki/Q5438153","display_name":"Fault detection and isolation","level":3,"score":0.27000001072883606},{"id":"https://openalex.org/C126422989","wikidata":"https://www.wikidata.org/wiki/Q93586","display_name":"Feature detection (computer vision)","level":4,"score":0.2526000142097473},{"id":"https://openalex.org/C64869954","wikidata":"https://www.wikidata.org/wiki/Q1859747","display_name":"False positive paradox","level":2,"score":0.25119999051094055}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/access.2026.3660103","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3660103","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1109/access.2026.3660103","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3660103","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.6251895427703857,"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W1996118086","https://openalex.org/W2948982773","https://openalex.org/W2963351448","https://openalex.org/W2998353611","https://openalex.org/W3009134234","https://openalex.org/W3096831136","https://openalex.org/W3147184966","https://openalex.org/W3160275635","https://openalex.org/W3166166117","https://openalex.org/W3169651898","https://openalex.org/W3174807077","https://openalex.org/W3195969653","https://openalex.org/W4214694907","https://openalex.org/W4292828873","https://openalex.org/W4311415873","https://openalex.org/W4312298392","https://openalex.org/W4312605624","https://openalex.org/W4313002532","https://openalex.org/W4385245566","https://openalex.org/W4385805211","https://openalex.org/W4386065385","https://openalex.org/W4390875033","https://openalex.org/W4393158476","https://openalex.org/W4402716375"],"related_works":[],"abstract_inverted_index":{"Few-shot":[0],"anomaly":[1,24,149],"detection":[2,150],"plays":[3],"a":[4,14,90,96,110,158],"crucial":[5],"role":[6],"in":[7,10,161],"automation":[8],"inspection":[9],"industry.":[11],"With":[12],"only":[13,153],"minimal":[15],"number":[16],"of":[17],"normal":[18,155],"samples,":[19,156],"this":[20,82],"method":[21,171,199],"can":[22],"achieve":[23],"identification":[25],"and":[26,45,74,95,104,113,118,133,175,187],"localization.":[27],"Recent":[28],"research":[29],"has":[30,38],"demonstrated":[31],"that":[32,144],"vision-language":[33],"models\u2014such":[34],"as":[35,56],"CLIP,":[36],"which":[37,88],"undergone":[39],"contrastive":[40],"language\u2013image":[41],"pre-training\u2014exhibit":[42],"strong":[43],"zero":[44],"few-shot":[46,148],"generalization":[47],"capabilities.":[48,106],"However,":[49],"existing":[50,147],"CLIP-based":[51],"methods":[52,151],"present":[53],"issues,":[54],"such":[55],"an":[57],"inability":[58],"to":[59,100,130],"fully":[60],"leverage":[61],"invariant":[62],"image":[63,174],"features,":[64],"interference":[65],"from":[66],"feature":[67,102,115],"noise,":[68],"reliance":[69],"on":[70,125,140],"manual":[71],"prompt":[72,112,131],"design,":[73],"unstable":[75],"image-level":[76,135,201],"scoring.":[77,136],"To":[78],"address":[79],"these":[80],"challenges,":[81],"paper":[83],"proposes":[84],"the":[85,121,167,179,191],"CLIP-Core":[86],"model,":[87],"integrates":[89],"Mahalanobis":[91],"distance":[92],"memory":[93],"bank":[94],"trainable":[97,111],"linear":[98],"layer":[99],"enhance":[101],"measurement":[103],"discrimination":[105],"Additionally,":[107],"we":[108],"introduce":[109],"text":[114],"reuse":[116],"mechanism":[117],"further":[119],"propose":[120],"CLIP-Core+":[122,145],"model":[123],"based":[124],"CLIP-Core,":[126],"resolving":[127],"problems":[128],"related":[129],"design":[132],"inadequate":[134],"The":[137],"experimental":[138],"results":[139],"public":[141],"datasets":[142],"demonstrate":[143],"outperforms":[146],"with":[152],"four":[154],"achieving":[157],"12.3%":[159],"improvement":[160],"pixel-level":[162,176],"Average":[163],"Precision":[164],"(AP).":[165],"On":[166],"CSAD":[168],"dataset,":[169,196],"our":[170,197],"improves":[172],"both":[173],"area":[177],"under":[178],"receiver":[180],"operating":[181],"characteristic":[182],"curve":[183],"(Auroc)":[184],"by":[185,203],"7%":[186],"10.1%,":[188],"respectively.":[189],"In":[190],"glass":[192],"bottle":[193],"appearance":[194],"defect":[195],"proposed":[198],"enhances":[200],"Auroc":[202],"7.1%.":[204]},"counts_by_year":[],"updated_date":"2026-02-13T13:36:01.753593","created_date":"2026-02-04T00:00:00"}
