{"id":"https://openalex.org/W7151890947","doi":"https://doi.org/10.48550/arxiv.2604.05632","title":"SGANet: Semantic and Geometric Alignment for Multimodal Multi-view Anomaly Detection","display_name":"SGANet: Semantic and Geometric Alignment for Multimodal Multi-view Anomaly Detection","publication_year":2026,"publication_date":"2026-04-07","ids":{"openalex":"https://openalex.org/W7151890947","doi":"https://doi.org/10.48550/arxiv.2604.05632"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.05632","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.05632","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":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2604.05632","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5133205489","display_name":"Letian Bai","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bai, Letian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133168586","display_name":"Chengyu Tao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tao, Chengyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5133228826","display_name":"Juan Du","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Du, Juan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"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":0.9474999904632568,"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":0.9474999904632568,"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":0.02199999988079071,"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":0.0052999998442828655,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.7246000170707703},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.692300021648407},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.5949000120162964},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5378000140190125},{"id":"https://openalex.org/keywords/anomaly","display_name":"Anomaly (physics)","score":0.5077000260353088},{"id":"https://openalex.org/keywords/semantic-feature","display_name":"Semantic feature","score":0.45159998536109924},{"id":"https://openalex.org/keywords/viewpoints","display_name":"Viewpoints","score":0.43689998984336853},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.42980000376701355}],"concepts":[{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.7246000170707703},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7218999862670898},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.692300021648407},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6047999858856201},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.5949000120162964},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5378000140190125},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.5077000260353088},{"id":"https://openalex.org/C2781122975","wikidata":"https://www.wikidata.org/wiki/Q16928266","display_name":"Semantic feature","level":2,"score":0.45159998536109924},{"id":"https://openalex.org/C2776035091","wikidata":"https://www.wikidata.org/wiki/Q7928819","display_name":"Viewpoints","level":2,"score":0.43689998984336853},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.42980000376701355},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.4058000147342682},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.3693999946117401},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.35929998755455017},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3244999945163727},{"id":"https://openalex.org/C2780226545","wikidata":"https://www.wikidata.org/wiki/Q6888030","display_name":"Modality (human\u2013computer interaction)","level":2,"score":0.32330000400543213},{"id":"https://openalex.org/C101814296","wikidata":"https://www.wikidata.org/wiki/Q5439685","display_name":"Feature model","level":3,"score":0.3131999969482422},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.30970001220703125},{"id":"https://openalex.org/C2779903281","wikidata":"https://www.wikidata.org/wiki/Q6888026","display_name":"Modalities","level":2,"score":0.272599995136261},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.26420000195503235},{"id":"https://openalex.org/C90312973","wikidata":"https://www.wikidata.org/wiki/Q7449052","display_name":"Semantic data model","level":2,"score":0.2500999867916107}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.05632","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.05632","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":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.05632","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.05632","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":"Preprint"},"sustainable_development_goals":[{"score":0.4828144907951355,"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Multi-view":[0],"anomaly":[1,52,142,165],"detection":[2,53,143,166],"aims":[3],"to":[4,61,92],"identify":[5],"surface":[6],"defects":[7],"on":[8,151],"complex":[9],"objects":[10],"using":[11],"observations":[12],"captured":[13],"from":[14,23,27,89],"multiple":[15],"viewpoints.":[16,125],"However,":[17],"existing":[18],"unsupervised":[19],"methods":[20],"often":[21],"suffer":[22],"feature":[24,65,95,129],"inconsistency":[25],"arising":[26],"viewpoint":[28,112],"variations":[29],"and":[30,41,58,69,132,135,154,167],"modality":[31],"discrepancies.":[32],"To":[33],"address":[34],"these":[35],"challenges,":[36],"we":[37],"propose":[38],"a":[39,46],"Semantic":[40],"Geometric":[42,116],"Alignment":[43,100,117],"Network":[44],"(SGANet),":[45],"unified":[47],"framework":[48],"for":[49],"multimodal":[50,146],"multi-view":[51,147],"that":[54,158],"effectively":[55,140],"combines":[56],"semantic":[57,103,131],"geometric":[59,137],"alignment":[60,104],"learn":[62],"physically":[63],"coherent":[64],"representations":[66],"across":[67,105,124],"viewpoints":[68],"modalities.":[70],"SGANet":[71,139,159],"consists":[72],"of":[73],"three":[74],"key":[75],"components.":[76],"The":[77,97,114],"Selective":[78],"Cross-view":[79],"Feature":[80],"Refinement":[81],"Module":[82],"(SCFRM)":[83],"selectively":[84],"aggregates":[85],"informative":[86],"patch":[87],"features":[88],"adjacent":[90],"views":[91],"enhance":[93],"cross-view":[94],"interaction.":[96],"Semantic-Structural":[98],"Patch":[99],"(SSPA)":[101],"enforces":[102],"modalities":[106],"while":[107],"maintaining":[108],"structural":[109,133],"consistency":[110],"under":[111],"transformations.":[113],"Multi-View":[115],"(MVGA)":[118],"further":[119],"aligns":[120],"geometrically":[121],"corresponding":[122],"patches":[123],"By":[126],"jointly":[127],"modeling":[128],"interaction,":[130],"consistency,":[134],"global":[136],"correspondence,":[138],"enhances":[141],"performance":[144,162],"in":[145,163,172],"settings.":[148],"Extensive":[149],"experiments":[150],"the":[152],"SiM3D":[153],"Eyecandies":[155],"datasets":[156],"demonstrate":[157],"achieves":[160],"state-of-the-art":[161],"both":[164],"localization,":[168],"validating":[169],"its":[170],"effectiveness":[171],"realistic":[173],"industrial":[174],"scenarios.":[175]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-04-09T00:00:00"}