{"id":"https://openalex.org/W4411631885","doi":"https://doi.org/10.1145/3731715.3733458","title":"UMLLA-AD: Mamba-Driven Adaptive Feature Selection for Industrial Anomaly Detection","display_name":"UMLLA-AD: Mamba-Driven Adaptive Feature Selection for Industrial Anomaly Detection","publication_year":2025,"publication_date":"2025-06-25","ids":{"openalex":"https://openalex.org/W4411631885","doi":"https://doi.org/10.1145/3731715.3733458"},"language":"en","primary_location":{"id":"doi:10.1145/3731715.3733458","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3731715.3733458","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2025 International Conference on Multimedia Retrieval","raw_type":"proceedings-article"},"type":"conference-paper","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":null,"display_name":"Tingting Fang","orcid":"https://orcid.org/0009-0008-2048-1899"},"institutions":[{"id":"https://openalex.org/I142108993","display_name":"Southwest University","ror":"https://ror.org/01kj4z117","country_code":"CN","type":"education","lineage":["https://openalex.org/I142108993"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tingting Fang","raw_affiliation_strings":["College of Artificial Intelligence, Southwest University, Chongqing, China"],"raw_orcid":"https://orcid.org/0009-0008-2048-1899","affiliations":[{"raw_affiliation_string":"College of Artificial Intelligence, Southwest University, Chongqing, China","institution_ids":["https://openalex.org/I142108993"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Junjie Wang","orcid":"https://orcid.org/0009-0003-5012-3543"},"institutions":[{"id":"https://openalex.org/I50632499","display_name":"Chongqing University of Technology","ror":"https://ror.org/04vgbd477","country_code":"CN","type":"education","lineage":["https://openalex.org/I50632499"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Junjie Wang","raw_affiliation_strings":["College of Computer Science and Engineering, Chongqing University of Technology, Chongqing, China"],"raw_orcid":"https://orcid.org/0009-0003-5012-3543","affiliations":[{"raw_affiliation_string":"College of Computer Science and Engineering, Chongqing University of Technology, Chongqing, China","institution_ids":["https://openalex.org/I50632499"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101476041","display_name":"Ming Ye","orcid":"https://orcid.org/0000-0001-5546-2593"},"institutions":[{"id":"https://openalex.org/I142108993","display_name":"Southwest University","ror":"https://ror.org/01kj4z117","country_code":"CN","type":"education","lineage":["https://openalex.org/I142108993"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ming Ye","raw_affiliation_strings":["College of Artificial Intelligence, Southwest University, Chongqing, China"],"raw_orcid":"https://orcid.org/0000-0001-5546-2593","affiliations":[{"raw_affiliation_string":"College of Artificial Intelligence, Southwest University, Chongqing, China","institution_ids":["https://openalex.org/I142108993"]}]},{"author_position":"last","author":{"id":null,"display_name":"Yuefei Huang","orcid":"https://orcid.org/0009-0004-2130-7578"},"institutions":[{"id":"https://openalex.org/I142108993","display_name":"Southwest University","ror":"https://ror.org/01kj4z117","country_code":"CN","type":"education","lineage":["https://openalex.org/I142108993"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuefei Huang","raw_affiliation_strings":["College of Artificial Intelligence, Southwest University, Chongqing, China"],"raw_orcid":"https://orcid.org/0009-0004-2130-7578","affiliations":[{"raw_affiliation_string":"College of Artificial Intelligence, Southwest University, Chongqing, China","institution_ids":["https://openalex.org/I142108993"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":5.019,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.95218827,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"294","last_page":"302"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":1.0,"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":1.0,"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/T10876","display_name":"Fault Detection and Control Systems","score":0.9749000072479248,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems 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/T12391","display_name":"Artificial Immune Systems Applications","score":0.9656000137329102,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"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/anomaly-detection","display_name":"Anomaly detection","score":0.7491812705993652},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.5593450665473938},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.555587112903595},{"id":"https://openalex.org/keywords/anomaly","display_name":"Anomaly (physics)","score":0.47288602590560913},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.4532838463783264},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4157591760158539},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.39768072962760925},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.36118584871292114},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.04900997877120972}],"concepts":[{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.7491812705993652},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.5593450665473938},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.555587112903595},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.47288602590560913},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.4532838463783264},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4157591760158539},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.39768072962760925},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.36118584871292114},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.04900997877120972},{"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/C26873012","wikidata":"https://www.wikidata.org/wiki/Q214781","display_name":"Condensed matter physics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3731715.3733458","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3731715.3733458","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2025 International Conference on Multimedia Retrieval","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":30,"referenced_works":["https://openalex.org/W2019307057","https://openalex.org/W2108598243","https://openalex.org/W2342543219","https://openalex.org/W2948982773","https://openalex.org/W3034314048","https://openalex.org/W3034986516","https://openalex.org/W3147184966","https://openalex.org/W3159648608","https://openalex.org/W3169651898","https://openalex.org/W3209793239","https://openalex.org/W3212044949","https://openalex.org/W4214694907","https://openalex.org/W4281643792","https://openalex.org/W4292851291","https://openalex.org/W4312239247","https://openalex.org/W4312605624","https://openalex.org/W4312772600","https://openalex.org/W4385195017","https://openalex.org/W4386065608","https://openalex.org/W4386065890","https://openalex.org/W4386075837","https://openalex.org/W4390875033","https://openalex.org/W4392139256","https://openalex.org/W4393147759","https://openalex.org/W4402444806","https://openalex.org/W4402716302","https://openalex.org/W4402878610","https://openalex.org/W4405498435","https://openalex.org/W6606363522","https://openalex.org/W6838461383"],"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/W2667207928","https://openalex.org/W2912112202","https://openalex.org/W4377864969","https://openalex.org/W3120251014"],"abstract_inverted_index":{"In":[0],"modern":[1],"industrial":[2,141,193],"environments,":[3],"anomaly":[4,37,87,125,194],"detection":[5],"is":[6],"a":[7,113,174],"critical":[8,86],"technology":[9],"for":[10,49,192],"ensuring":[11],"production":[12],"quality":[13],"and":[14,29,51,77,127,147,173,189],"safety.":[15],"However,":[16],"existing":[17],"methods":[18],"face":[19],"the":[20,93,104,109,121,132,162],"challenge":[21],"of":[22,108,171,177],"information":[23,78],"redundancy":[24,79],"when":[25],"handling":[26],"high-dimensional":[27],"data,":[28],"traditional":[30],"autoencoders":[31],"struggle":[32],"to":[33,123],"effectively":[34],"capture":[35],"subtle":[36,124],"features.":[38],"To":[39],"address":[40],"these":[41],"limitations,":[42],"we":[43,63,90],"propose":[44],"UMLLA-AD,":[45],"an":[46,66,168,187],"efficient":[47,188],"network":[48],"detecting":[50],"localizing":[52],"anomalies.":[53],"UMLLA-AD":[54,150],"attains":[55],"performance":[56],"enhancement":[57],"via":[58],"two":[59],"core":[60],"innovations.":[61],"Firstly,":[62],"have":[64,91],"designed":[65],"Adaptive":[67],"Feature":[68],"Channel":[69],"Selection":[70],"(AFCS)":[71],"mechanism":[72,98],"that":[73,183],"significantly":[74],"reduces":[75],"noise":[76],"by":[80],"dynamically":[81],"screening":[82],"feature":[83],"channels":[84],"containing":[85],"information.":[88],"Secondly,":[89],"developed":[92],"UNet-based":[94],"Mamba-like":[95],"linear":[96,115],"attention":[97,116],"(UMLLA)":[99],"reconstruction":[100,129],"network,":[101],"which":[102],"combines":[103],"global":[105],"modeling":[106],"capability":[107],"Mamba":[110],"architecture":[111],"with":[112],"multi-head":[114],"mechanism.":[117],"This":[118],"integration":[119],"enhances":[120],"sensitivity":[122],"features":[126],"improves":[128],"accuracy":[130],"within":[131],"UNet":[133],"framework.":[134],"We":[135],"conducted":[136],"extensive":[137],"experiments":[138],"on":[139,161],"several":[140],"datasets,":[142],"including":[143],"MVTec-AD,":[144],"BTAD,":[145],"MPDD,":[146],"VisA.":[148],"The":[149],"framework":[151],"showed":[152],"significant":[153],"competitive":[154],"advantages":[155],"in":[156],"all":[157],"datasets":[158],"tested.":[159],"Particularly":[160],"MVTec-AD":[163],"dataset,":[164],"our":[165],"approach":[166],"achieved":[167],"Image":[169],"AUROC":[170,176],"99.7%":[172],"Pixel":[175],"99.1%.":[178],"These":[179],"results":[180],"clearly":[181],"demonstrate":[182],"this":[184],"research":[185],"provides":[186],"robust":[190],"solution":[191],"detection.":[195]},"counts_by_year":[{"year":2026,"cited_by_count":3}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
