{"id":"https://openalex.org/W4387123717","doi":"https://doi.org/10.1109/case56687.2023.10260493","title":"Cross-Modal Self-Supervised Feature Extraction for Anomaly Detection in Human Monitoring","display_name":"Cross-Modal Self-Supervised Feature Extraction for Anomaly Detection in Human Monitoring","publication_year":2023,"publication_date":"2023-08-26","ids":{"openalex":"https://openalex.org/W4387123717","doi":"https://doi.org/10.1109/case56687.2023.10260493"},"language":"en","primary_location":{"is_oa":false,"landing_page_url":"https://doi.org/10.1109/case56687.2023.10260493","pdf_url":null,"source":{"id":"https://openalex.org/S4363607892","display_name":"2022 IEEE 18th International Conference on Automation Science and Engineering (CASE)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false},"type":"article","type_crossref":"proceedings-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/A5092958517","display_name":"Jose Alejandro Avellaneda","orcid":null},"institutions":[{"id":"https://openalex.org/I135598925","display_name":"Kyushu University","ror":"https://ror.org/00p4k0j84","country_code":"JP","type":"education","lineage":["https://openalex.org/I135598925"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Jose Alejandro Avellaneda","raw_affiliation_strings":["ISEE, University of Kyushu,Fukuoka,Japan,819-0395"],"affiliations":[{"raw_affiliation_string":"ISEE, University of Kyushu,Fukuoka,Japan,819-0395","institution_ids":["https://openalex.org/I135598925"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5090155504","display_name":"Tetsu Matsukawa","orcid":"https://orcid.org/0000-0002-8841-6304"},"institutions":[{"id":"https://openalex.org/I135598925","display_name":"Kyushu University","ror":"https://ror.org/00p4k0j84","country_code":"JP","type":"education","lineage":["https://openalex.org/I135598925"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Tetsu Matsukawa","raw_affiliation_strings":["ISEE, University of Kyushu,Fukuoka,Japan,819-0395"],"affiliations":[{"raw_affiliation_string":"ISEE, University of Kyushu,Fukuoka,Japan,819-0395","institution_ids":["https://openalex.org/I135598925"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5049568882","display_name":"Einoshin Suzuki","orcid":"https://orcid.org/0000-0001-7743-6177"},"institutions":[{"id":"https://openalex.org/I135598925","display_name":"Kyushu University","ror":"https://ror.org/00p4k0j84","country_code":"JP","type":"education","lineage":["https://openalex.org/I135598925"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Einoshin Suzuki","raw_affiliation_strings":["ISEE, University of Kyushu,Fukuoka,Japan,819-0395"],"affiliations":[{"raw_affiliation_string":"ISEE, University of Kyushu,Fukuoka,Japan,819-0395","institution_ids":["https://openalex.org/I135598925"]}]}],"institution_assertions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":0,"max":68},"biblio":{"volume":"9","issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection in High-Dimensional Data","score":0.9999,"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 in High-Dimensional Data","score":0.9999,"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/T11819","display_name":"Digital Epidemiology and Disease Surveillance","score":0.9912,"subfield":{"id":"https://openalex.org/subfields/2713","display_name":"Epidemiology"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T10812","display_name":"Human Action Recognition and Pose Estimation","score":0.9905,"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/modalities","display_name":"Modalities","score":0.63751066},{"id":"https://openalex.org/keywords/outlier-detection","display_name":"Outlier Detection","score":0.588927},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly Detection","score":0.56793},{"id":"https://openalex.org/keywords/spatiotemporal-features","display_name":"Spatiotemporal Features","score":0.523077},{"id":"https://openalex.org/keywords/action-recognition","display_name":"Action Recognition","score":0.50379}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7623061},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.66922295},{"id":"https://openalex.org/C2779903281","wikidata":"https://www.wikidata.org/wiki/Q6888026","display_name":"Modalities","level":2,"score":0.63751066},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.60356104},{"id":"https://openalex.org/C71139939","wikidata":"https://www.wikidata.org/wiki/Q910194","display_name":"Modal","level":2,"score":0.5853153},{"id":"https://openalex.org/C32834561","wikidata":"https://www.wikidata.org/wiki/Q660730","display_name":"Subspace topology","level":2,"score":0.5440426},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.53763056},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5313314},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.527945},{"id":"https://openalex.org/C152139883","wikidata":"https://www.wikidata.org/wiki/Q252973","display_name":"Mutual information","level":2,"score":0.43434367},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.37739703},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.33910698},{"id":"https://openalex.org/C36289849","wikidata":"https://www.wikidata.org/wiki/Q34749","display_name":"Social science","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0},{"id":"https://openalex.org/C188027245","wikidata":"https://www.wikidata.org/wiki/Q750446","display_name":"Polymer chemistry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"is_oa":false,"landing_page_url":"https://doi.org/10.1109/case56687.2023.10260493","pdf_url":null,"source":{"id":"https://openalex.org/S4363607892","display_name":"2022 IEEE 18th International Conference on Automation Science and Engineering (CASE)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false}],"best_oa_location":null,"sustainable_development_goals":[],"grants":[],"datasets":[],"versions":[],"referenced_works_count":49,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1529028887","https://openalex.org/W1574901103","https://openalex.org/W1686810756","https://openalex.org/W1965555277","https://openalex.org/W2117539524","https://openalex.org/W2141245797","https://openalex.org/W2187089797","https://openalex.org/W2194775991","https://openalex.org/W2535714289","https://openalex.org/W2559927751","https://openalex.org/W2618530766","https://openalex.org/W2787947370","https://openalex.org/W2883725317","https://openalex.org/W2886641317","https://openalex.org/W2890707978","https://openalex.org/W2896457183","https://openalex.org/W2909206463","https://openalex.org/W2914570111","https://openalex.org/W2944250323","https://openalex.org/W2963045681","https://openalex.org/W2963758027","https://openalex.org/W2977916740","https://openalex.org/W2987228832","https://openalex.org/W2992430691","https://openalex.org/W3005680577","https://openalex.org/W3011858853","https://openalex.org/W3015799890","https://openalex.org/W3035060554","https://openalex.org/W3035207996","https://openalex.org/W3036224891","https://openalex.org/W3088241762","https://openalex.org/W3096804376","https://openalex.org/W3115293622","https://openalex.org/W3117272077","https://openalex.org/W3129082918","https://openalex.org/W3129999151","https://openalex.org/W3135550350","https://openalex.org/W3158714121","https://openalex.org/W3166396011","https://openalex.org/W3171488456","https://openalex.org/W3209311154","https://openalex.org/W3209346713","https://openalex.org/W4206839319","https://openalex.org/W4287252542","https://openalex.org/W4288079366","https://openalex.org/W4294170691","https://openalex.org/W4317821607","https://openalex.org/W4385245566"],"related_works":["https://openalex.org/W4301143707","https://openalex.org/W2952745240","https://openalex.org/W2466816617","https://openalex.org/W2364594919","https://openalex.org/W2185469136","https://openalex.org/W2167092671","https://openalex.org/W2011264131","https://openalex.org/W1980381208","https://openalex.org/W1970834875","https://openalex.org/W1861706286"],"abstract_inverted_index":{"This":[0],"paper":[1],"proposes":[2],"to":[3,8,23,96,110,115,123,128],"extract":[4],"cross-modal":[5,54],"self-supervised":[6,50],"features":[7,37,55],"detect":[9],"anomalies":[10],"in":[11,21,40,64,88],"human":[12],"monitoring.":[13],"Our":[14],"previous":[15],"works":[16],"that":[17,52],"use":[18,30],"deep":[19],"captioning":[20],"addition":[22],"monitoring":[24],"images":[25],"were":[26],"successful.":[27],"However,":[28],"their":[29],"of":[31,80],"unimodally":[32],"trained":[33],"image":[34],"and":[35,91,112,125],"text":[36],"shows":[38],"deficiencies":[39],"capturing":[41,70],"contextual":[42],"information":[43,60],"across":[44],"the":[45,58,77,97],"modalities.":[46],"We":[47],"devise":[48],"a":[49,65],"method":[51],"creates":[53],"by":[56],"maximizing":[57],"mutual":[59],"between":[61,74],"both":[62,89],"modalities":[63],"common":[66],"subspace.":[67],"It":[68],"allows":[69],"different":[71],"complex":[72],"distributions":[73],"modalities,":[75],"improving":[76],"detection":[78],"performance":[79],"clustering":[81],"methods.":[82],"Extensive":[83],"experimental":[84],"results":[85],"show":[86],"improvements":[87],"AUC":[90,105],"AUPRC":[92,118],"scores":[93],"when":[94],"compared":[95],"best":[98],"baselines":[99],"on":[100],"two":[101],"real-world":[102],"datasets.":[103],"The":[104,117],"has":[106,119],"improved":[107,120],"from":[108,113,121,126],"0.895":[109],"0.969,":[111],"0.97":[114],"0.98.":[116],"0.681":[122],"0.850,":[124],"0.840":[127],"0.894.":[129]},"cited_by_api_url":"https://api.openalex.org/works?filter=cites:W4387123717","counts_by_year":[],"updated_date":"2024-12-03T12:31:06.222754","created_date":"2023-09-29"}