{"id":"https://openalex.org/W7133522288","doi":"https://doi.org/10.48550/arxiv.2603.02629","title":"Towards an Incremental Unified Multimodal Anomaly Detection: Augmenting Multimodal Denoising From an Information Bottleneck Perspective","display_name":"Towards an Incremental Unified Multimodal Anomaly Detection: Augmenting Multimodal Denoising From an Information Bottleneck Perspective","publication_year":2026,"publication_date":"2026-03-03","ids":{"openalex":"https://openalex.org/W7133522288","doi":"https://doi.org/10.48550/arxiv.2603.02629"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.02629","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.02629","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.02629","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5072081771","display_name":"Kaifang Long","orcid":"https://orcid.org/0000-0002-9460-0052"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Long, Kaifang","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128092434","display_name":"Lianbo Ma","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ma, Lianbo","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128102368","display_name":"Jiaqi Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Jiaqi","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128098268","display_name":"Liming Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Liming","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5128110623","display_name":"Guoyang Xie","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xie, Guoyang","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5072081771"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9035000205039978,"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":0.9035000205039978,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.06620000302791595,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.003100000089034438,"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/spurious-relationship","display_name":"Spurious relationship","score":0.7208999991416931},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.6093999743461609},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.5920000076293945},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.5683000087738037},{"id":"https://openalex.org/keywords/bottleneck","display_name":"Bottleneck","score":0.5368000268936157},{"id":"https://openalex.org/keywords/interpretability","display_name":"Interpretability","score":0.5354999899864197},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.5034000277519226},{"id":"https://openalex.org/keywords/perspective","display_name":"Perspective (graphical)","score":0.43619999289512634}],"concepts":[{"id":"https://openalex.org/C97256817","wikidata":"https://www.wikidata.org/wiki/Q1462316","display_name":"Spurious relationship","level":2,"score":0.7208999991416931},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7127000093460083},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6093999743461609},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.5920000076293945},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.5683000087738037},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5407999753952026},{"id":"https://openalex.org/C2780513914","wikidata":"https://www.wikidata.org/wiki/Q18210350","display_name":"Bottleneck","level":2,"score":0.5368000268936157},{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.5354999899864197},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.5034000277519226},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4984000027179718},{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.43619999289512634},{"id":"https://openalex.org/C60008888","wikidata":"https://www.wikidata.org/wiki/Q6031013","display_name":"Information bottleneck method","level":3,"score":0.43320000171661377},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.40130001306533813},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.4002000093460083},{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.3837999999523163},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.3828999996185303},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.3749000132083893},{"id":"https://openalex.org/C7149132","wikidata":"https://www.wikidata.org/wiki/Q1377840","display_name":"Forgetting","level":2,"score":0.3499999940395355},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.3052999973297119},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.27709999680519104},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.26660001277923584},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.26179999113082886},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.25450000166893005}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.02629","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.02629","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.02629","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.02629","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities","score":0.7489571571350098}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"The":[0],"quest":[1],"for":[2],"incremental":[3,26,93],"unified":[4,94],"multimodal":[5,103],"anomaly":[6],"detection":[7],"seeks":[8],"to":[9,17,28,33,55,115,146,160],"empower":[10],"a":[11,59,123],"single":[12],"model":[13],"with":[14],"the":[15,38,64,81,102,131,135,143,157,166,188],"ability":[16],"systematically":[18],"detect":[19],"anomalies":[20],"across":[21],"all":[22],"categories":[23],"and":[24,69,86,96,138,179,184,190],"support":[25],"learning":[27],"accommodate":[29],"emerging":[30],"objects/categories.":[31],"Central":[32],"this":[34,57,76,90,119],"pursuit":[35],"is":[36,112],"resolving":[37],"catastrophic":[39,73],"forgetting":[40],"dilemma,":[41,58],"which":[42,129],"involves":[43],"acquiring":[44],"new":[45],"knowledge":[46],"while":[47],"preserving":[48,171],"prior":[49],"learned":[50],"knowledge.":[51],"Despite":[52],"some":[53],"efforts":[54],"address":[56,118],"key":[60],"oversight":[61],"persists:":[62],"ignoring":[63],"potential":[65],"impact":[66],"of":[67,84,109,134,176,193],"spurious":[68,85,152],"redundant":[70,87,163],"features":[71,88,164],"on":[72,89,181],"forgetting.":[74,116],"In":[75],"paper,":[77],"we":[78,121],"delve":[79],"into":[80],"negative":[82],"effect":[83],"dilemma":[91],"in":[92],"frameworks,":[95],"reveal":[97],"that":[98],"under":[99],"similar":[100],"conditions,":[101],"framework":[104,126],"developed":[105],"by":[106],"naive":[107],"aggregation":[108],"unimodal":[110],"architectures":[111],"more":[113],"prone":[114],"To":[117],"issue,":[120],"introduce":[122],"novel":[124],"denoising":[125],"called":[127],"IB-IUMAD,":[128],"exploits":[130],"complementary":[132],"benefits":[133],"Mamba":[136],"decoder":[137],"information":[139],"bottleneck":[140],"fusion":[141],"module:":[142],"former":[144],"dedicated":[145],"disentangle":[147],"inter-object":[148],"feature":[149,153],"coupling,":[150],"preventing":[151],"interference":[154],"between":[155],"objects;":[156],"latter":[158],"serves":[159],"filter":[161],"out":[162],"from":[165],"fused":[167],"features,":[168],"thus":[169],"explicitly":[170],"discriminative":[172],"information.":[173],"A":[174],"series":[175],"theoretical":[177],"analyses":[178],"experiments":[180],"MVTec":[182],"3D-AD":[183],"Eyecandies":[185],"datasets":[186],"demonstrates":[187],"effectiveness":[189],"competitive":[191],"performance":[192],"IB-IUMAD.":[194]},"counts_by_year":[],"updated_date":"2026-03-05T07:36:02.291473","created_date":"2026-03-05T00:00:00"}