{"id":"https://openalex.org/W4402915921","doi":"https://doi.org/10.1109/icip51287.2024.10647894","title":"Surface Anomaly Detection With Anomalous Feature Restriction And Difference-Aware Enhancement","display_name":"Surface Anomaly Detection With Anomalous Feature Restriction And Difference-Aware Enhancement","publication_year":2024,"publication_date":"2024-09-27","ids":{"openalex":"https://openalex.org/W4402915921","doi":"https://doi.org/10.1109/icip51287.2024.10647894"},"language":"en","primary_location":{"id":"doi:10.1109/icip51287.2024.10647894","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip51287.2024.10647894","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-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/A5026733694","display_name":"Jinhui Zhao","orcid":"https://orcid.org/0000-0003-2509-7550"},"institutions":[{"id":"https://openalex.org/I90610280","display_name":"South China University of Technology","ror":"https://ror.org/0530pts50","country_code":"CN","type":"education","lineage":["https://openalex.org/I90610280"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jinhui Zhao","raw_affiliation_strings":["South China University of Technology,Guangzhou,China"],"affiliations":[{"raw_affiliation_string":"South China University of Technology,Guangzhou,China","institution_ids":["https://openalex.org/I90610280"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049629115","display_name":"Hongxia Gao","orcid":"https://orcid.org/0000-0001-8142-7011"},"institutions":[{"id":"https://openalex.org/I90610280","display_name":"South China University of Technology","ror":"https://ror.org/0530pts50","country_code":"CN","type":"education","lineage":["https://openalex.org/I90610280"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hongxia Gao","raw_affiliation_strings":["South China University of Technology,Guangzhou,China"],"affiliations":[{"raw_affiliation_string":"South China University of Technology,Guangzhou,China","institution_ids":["https://openalex.org/I90610280"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100638104","display_name":"Tongtong Liu","orcid":"https://orcid.org/0009-0008-7649-7291"},"institutions":[{"id":"https://openalex.org/I90610280","display_name":"South China University of Technology","ror":"https://ror.org/0530pts50","country_code":"CN","type":"education","lineage":["https://openalex.org/I90610280"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tongtong Liu","raw_affiliation_strings":["South China University of Technology,Guangzhou,China"],"affiliations":[{"raw_affiliation_string":"South China University of Technology,Guangzhou,China","institution_ids":["https://openalex.org/I90610280"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5026733694"],"corresponding_institution_ids":["https://openalex.org/I90610280"],"apc_list":null,"apc_paid":null,"fwci":0.3637,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.67239806,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"1377","last_page":"1383"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9998000264167786,"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":0.9998000264167786,"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/T11241","display_name":"Advanced Malware Detection Techniques","score":0.9889000058174133,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T10400","display_name":"Network Security and Intrusion Detection","score":0.9818999767303467,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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":0.6574565768241882},{"id":"https://openalex.org/keywords/anomaly","display_name":"Anomaly (physics)","score":0.6567887663841248},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5550906658172607},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4973309338092804},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4242696464061737},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4191170930862427},{"id":"https://openalex.org/keywords/surface","display_name":"Surface (topology)","score":0.4135396182537079},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3548349142074585},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.19283294677734375},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.16904842853546143},{"id":"https://openalex.org/keywords/condensed-matter-physics","display_name":"Condensed matter physics","score":0.0681510865688324}],"concepts":[{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.6574565768241882},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.6567887663841248},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5550906658172607},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4973309338092804},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4242696464061737},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4191170930862427},{"id":"https://openalex.org/C2776799497","wikidata":"https://www.wikidata.org/wiki/Q484298","display_name":"Surface (topology)","level":2,"score":0.4135396182537079},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3548349142074585},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.19283294677734375},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.16904842853546143},{"id":"https://openalex.org/C26873012","wikidata":"https://www.wikidata.org/wiki/Q214781","display_name":"Condensed matter physics","level":1,"score":0.0681510865688324},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","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":1,"locations":[{"id":"doi:10.1109/icip51287.2024.10647894","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip51287.2024.10647894","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.46000000834465027,"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W1901129140","https://openalex.org/W2599354622","https://openalex.org/W2963351448","https://openalex.org/W2970648063","https://openalex.org/W3034155723","https://openalex.org/W3092704883","https://openalex.org/W3147184966","https://openalex.org/W3159648608","https://openalex.org/W3166166117","https://openalex.org/W3169651898","https://openalex.org/W3204332276","https://openalex.org/W3204520143","https://openalex.org/W4214694907","https://openalex.org/W4287887190","https://openalex.org/W4312239247","https://openalex.org/W4312772600","https://openalex.org/W4317513057","https://openalex.org/W4366493107","https://openalex.org/W4376626035","https://openalex.org/W4386075837","https://openalex.org/W6781627986","https://openalex.org/W6786940622","https://openalex.org/W6796934673","https://openalex.org/W6802573413","https://openalex.org/W6803919275","https://openalex.org/W7016021835"],"related_works":["https://openalex.org/W2806741695","https://openalex.org/W4290647774","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/W4377864969","https://openalex.org/W2972971679"],"abstract_inverted_index":{"In":[0,51],"industrial":[1],"automatic":[2],"product":[3],"quality":[4],"inspection,":[5],"visual":[6],"anomaly":[7,13,25,187],"detection":[8,14,161,188],"is":[9,139],"paramount.":[10],"While":[11],"unsupervised":[12],"methods":[15,28],"based":[16],"on":[17,173],"reconstruction":[18,40,133],"have":[19],"shown":[20],"promising":[21],"results,":[22,189],"particularly":[23],"in":[24,44,110,153,176],"localization,":[26],"these":[27],"still":[29],"suffer":[30],"from":[31,118,134],"challenges":[32],"such":[33],"as":[34],"overfitting":[35,119],"of":[36,126,146,163,170],"pseudo-anomalous":[37],"distribution":[38],"by":[39,48,106],"networks":[41],"and":[42,61,79,94,151,194],"difficulty":[43],"distinguishing":[45],"near-distribution":[46,166],"anomalies":[47],"discriminative":[49],"networks.":[50],"this":[52],"paper,":[53],"we":[54],"propose":[55],"a":[56,131],"novel":[57],"Anomalous":[58],"Feature":[59,90],"Restriction":[60,91],"Difference-Aware":[62,96],"Enhancement":[63,97],"Network":[64],"(RE-Net),":[65],"which":[66],"aims":[67],"to":[68,114,142],"constrain":[69],"abnormal":[70,80,104,152],"features":[71,105,109],"while":[72,122],"enhancing":[73],"the":[74,88,95,111,116,124,144,147,154,160,177],"minute":[75],"discrepancies":[76],"between":[77,149],"normal":[78,108,127,132,150],"features.":[81],"This":[82],"network":[83,117],"comprises":[84],"two":[85],"key":[86],"modules:":[87],"Abnormal":[89],"Module":[92,98],"(AFRM)":[93],"(DAEM).":[99],"AFRM":[100],"first":[101],"explicitly":[102],"constrains":[103],"utilizing":[107],"reconstructed":[112],"subnetwork":[113],"prevent":[115],"pseudo-abnormal":[120],"distributions":[121],"ensuring":[123],"consistency":[125],"regions.":[128],"Upon":[129],"achieving":[130],"an":[135],"anomalous":[136],"input,":[137],"DAEM":[138],"then":[140],"used":[141],"enhance":[143],"perception":[145],"difference":[148],"discriminant":[155],"subnetwork,":[156],"thereby":[157],"effectively":[158],"improving":[159],"ability":[162],"highly":[164],"camouflaged":[165],"anomalies.":[167],"A":[168],"series":[169],"comparative":[171],"experiments":[172],"textured":[174],"objects":[175],"MVTec":[178],"AD":[179],"dataset":[180],"show":[181],"that":[182],"our":[183],"method":[184],"achieves":[185],"better":[186],"reaching":[190],"99.9%":[191],"image-level":[192],"AUROC":[193],"98.76%":[195],"pixel-level":[196],"AUROC.":[197]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2025-12-21T23:12:01.093139","created_date":"2025-10-10T00:00:00"}
