{"id":"https://openalex.org/W4393079388","doi":"https://doi.org/10.1109/tip.2024.3378464","title":"Unsupervised Out-of-Distribution Object Detection via PCA-Driven Dynamic Prototype Enhancement","display_name":"Unsupervised Out-of-Distribution Object Detection via PCA-Driven Dynamic Prototype Enhancement","publication_year":2024,"publication_date":"2024-01-01","ids":{"openalex":"https://openalex.org/W4393079388","doi":"https://doi.org/10.1109/tip.2024.3378464","pmid":"https://pubmed.ncbi.nlm.nih.gov/38517717"},"language":"en","primary_location":{"id":"doi:10.1109/tip.2024.3378464","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tip.2024.3378464","pdf_url":null,"source":{"id":"https://openalex.org/S4210173141","display_name":"IEEE Transactions on Image Processing","issn_l":"1057-7149","issn":["1057-7149","1941-0042"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Image Processing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"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/A5103126895","display_name":"Aming Wu","orcid":null},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Aming Wu","raw_affiliation_strings":["School of Electronic Engineering, Xidian University, Xi&#x2019;an, China"],"affiliations":[{"raw_affiliation_string":"School of Electronic Engineering, Xidian University, Xi&#x2019;an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015874725","display_name":"Cheng Deng","orcid":"https://orcid.org/0000-0003-2620-3247"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Cheng Deng","raw_affiliation_strings":["School of Electronic Engineering, Xidian University, Xi&#x2019;an, China"],"affiliations":[{"raw_affiliation_string":"School of Electronic Engineering, Xidian University, Xi&#x2019;an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100431792","display_name":"Wei Liu","orcid":"https://orcid.org/0000-0002-3865-8145"},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wei Liu","raw_affiliation_strings":["Tencent Data Platform, Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"Tencent Data Platform, Shenzhen, China","institution_ids":["https://openalex.org/I2250653659"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5103126895"],"corresponding_institution_ids":["https://openalex.org/I149594827"],"apc_list":null,"apc_paid":null,"fwci":4.2803,"has_fulltext":false,"cited_by_count":17,"citation_normalized_percentile":{"value":0.95238212,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":99},"biblio":{"volume":"33","issue":null,"first_page":"2431","last_page":"2446"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9998999834060669,"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"}},"topics":[{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9998999834060669,"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"}},{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9988999962806702,"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":0.9987000226974487,"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/computer-science","display_name":"Computer science","score":0.7329422235488892},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.7292283773422241},{"id":"https://openalex.org/keywords/principal-component-analysis","display_name":"Principal component analysis","score":0.6624385118484497},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6480821371078491},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.6310819983482361},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5366117358207703},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.5257043242454529},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.46677130460739136},{"id":"https://openalex.org/keywords/cognitive-neuroscience-of-visual-object-recognition","display_name":"Cognitive neuroscience of visual object recognition","score":0.4237349033355713},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3532898426055908},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.338069885969162}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7329422235488892},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.7292283773422241},{"id":"https://openalex.org/C27438332","wikidata":"https://www.wikidata.org/wiki/Q2873","display_name":"Principal component analysis","level":2,"score":0.6624385118484497},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6480821371078491},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.6310819983482361},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5366117358207703},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.5257043242454529},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.46677130460739136},{"id":"https://openalex.org/C64876066","wikidata":"https://www.wikidata.org/wiki/Q5141226","display_name":"Cognitive neuroscience of visual object recognition","level":3,"score":0.4237349033355713},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3532898426055908},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.338069885969162}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tip.2024.3378464","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tip.2024.3378464","pdf_url":null,"source":{"id":"https://openalex.org/S4210173141","display_name":"IEEE Transactions on Image Processing","issn_l":"1057-7149","issn":["1057-7149","1941-0042"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Image Processing","raw_type":"journal-article"},{"id":"pmid:38517717","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/38517717","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE transactions on image processing : a publication of the IEEE Signal Processing Society","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G7156137471","display_name":null,"funder_award_id":"62102293","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G986958842","display_name":null,"funder_award_id":"62132016","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":80,"referenced_works":["https://openalex.org/W137179224","https://openalex.org/W967544008","https://openalex.org/W1515020792","https://openalex.org/W1831674524","https://openalex.org/W1861492603","https://openalex.org/W1992633833","https://openalex.org/W2031489346","https://openalex.org/W2047643928","https://openalex.org/W2078473970","https://openalex.org/W2114943652","https://openalex.org/W2115627867","https://openalex.org/W2117539524","https://openalex.org/W2140405352","https://openalex.org/W2194775991","https://openalex.org/W2335728318","https://openalex.org/W2732026016","https://openalex.org/W2758021822","https://openalex.org/W2888527098","https://openalex.org/W2889625178","https://openalex.org/W2891894830","https://openalex.org/W2913300775","https://openalex.org/W2934198733","https://openalex.org/W2962966271","https://openalex.org/W2963150697","https://openalex.org/W2963446712","https://openalex.org/W2963936013","https://openalex.org/W2964137095","https://openalex.org/W2976969310","https://openalex.org/W2997212544","https://openalex.org/W2997998901","https://openalex.org/W3006853338","https://openalex.org/W3034230713","https://openalex.org/W3034429256","https://openalex.org/W3035311832","https://openalex.org/W3035564946","https://openalex.org/W3041905672","https://openalex.org/W3090558831","https://openalex.org/W3118600296","https://openalex.org/W3118608800","https://openalex.org/W3120411988","https://openalex.org/W3122191883","https://openalex.org/W3176709420","https://openalex.org/W3208853938","https://openalex.org/W3213509545","https://openalex.org/W3217535672","https://openalex.org/W4200064219","https://openalex.org/W4205702529","https://openalex.org/W4214769349","https://openalex.org/W4238226374","https://openalex.org/W4287123660","https://openalex.org/W4288083516","https://openalex.org/W4312250316","https://openalex.org/W4312394134","https://openalex.org/W4312640451","https://openalex.org/W4312894362","https://openalex.org/W4313042927","https://openalex.org/W4319299833","https://openalex.org/W6620707391","https://openalex.org/W6625168331","https://openalex.org/W6638807622","https://openalex.org/W6683825394","https://openalex.org/W6728622933","https://openalex.org/W6745553787","https://openalex.org/W6745891213","https://openalex.org/W6752760542","https://openalex.org/W6754995574","https://openalex.org/W6757615711","https://openalex.org/W6758976440","https://openalex.org/W6774983715","https://openalex.org/W6779395778","https://openalex.org/W6780542504","https://openalex.org/W6780874654","https://openalex.org/W6784323503","https://openalex.org/W6788356050","https://openalex.org/W6801748121","https://openalex.org/W6809902071","https://openalex.org/W6840421078","https://openalex.org/W6843065753","https://openalex.org/W6843441237","https://openalex.org/W6947681574"],"related_works":["https://openalex.org/W2125652721","https://openalex.org/W1540371141","https://openalex.org/W4231274751","https://openalex.org/W1549363203","https://openalex.org/W2154063878","https://openalex.org/W2556012038","https://openalex.org/W1489772951","https://openalex.org/W1538046993","https://openalex.org/W2963610131","https://openalex.org/W4387272257"],"abstract_inverted_index":{"To":[0,201],"promote":[1],"the":[2,23,34,69,73,116,124,128,134,151,155,172,183,190,194,206,216,220,236,240,245,248,254,261,279],"application":[3],"of":[4,33,71,75,133,154,174,187,219,256,281],"object":[5,11,272],"detectors":[6,39],"in":[7],"real":[8],"scenes,":[9],"out-of-distribution":[10],"detection":[12,52],"(OOD-OD)":[13],"is":[14,37,160],"proposed":[15],"to":[16,22,61,94,101,115,139,163,210,230,234],"distinguish":[17],"whether":[18],"detected":[19],"objects":[20,188],"belong":[21],"ones":[24],"that":[25,38,112,144,148,214],"are":[26,113,120,137],"unseen":[27],"during":[28],"training":[29,107],"or":[30],"not.":[31],"One":[32],"key":[35],"challenges":[36],"lack":[40],"unknown":[41,176],"data":[42,64,105,167,243],"for":[43,65,106,122,168,179],"supervision,":[44],"and":[45,77,108,119,193,244,270],"as":[46],"a":[47,86,227],"result,":[48],"can":[49],"produce":[50],"overconfident":[51],"results":[53],"on":[54,268],"OOD":[55,63,79,104,142,158,166,242,258],"data.":[56,177],"Thus,":[57],"this":[58,82,202],"task":[59],"requires":[60],"synthesize":[62],"training,":[66,169],"which":[67,170,252],"achieves":[68],"goal":[70],"enhancing":[72],"ability":[74,255],"localizing":[76],"discriminating":[78,257],"objects.":[80,259],"In":[81,260],"paper,":[83],"we":[84,204,225,263],"propose":[85],"novel":[87],"method,":[88],"i.e.,":[89],"PCA-Driven":[90],"dynamic":[91,110,212],"prototype":[92],"enhancement,":[93],"explore":[95],"exploiting":[96],"Principal":[97],"Component":[98],"Analysis":[99],"(PCA)":[100],"extract":[102,164,211],"simulative":[103,165,241],"obtain":[109],"prototypes":[111,213,233],"related":[114],"current":[117,221],"input":[118],"helpful":[121],"boosting":[123],"discrimination":[125],"ability.":[126],"Concretely,":[127],"last":[129],"few":[130],"principal":[131,208,250],"components":[132,209],"backbone":[135,191,222],"features":[136,192,196,246],"utilized":[138],"calculate":[140],"an":[141],"map":[143,159],"involves":[145],"plentiful":[146],"information":[147,186,218],"deviates":[149],"from":[150,247],"correlation":[152],"distribution":[153],"input.":[156],"The":[157,274],"further":[161],"used":[162],"alleviates":[171],"impact":[173],"lacking":[175],"Besides,":[178],"in-distribution":[180],"(ID)":[181],"data,":[182],"category-level":[184],"semantic":[185,217,237],"between":[189,239],"high-level":[195],"should":[197],"be":[198],"kept":[199],"consistent.":[200],"end,":[203],"utilize":[205],"residual":[207,249],"reflect":[215],"features.":[223],"Next,":[224],"define":[226],"contrastive":[228],"loss":[229],"leverage":[231],"these":[232],"enlarge":[235],"gap":[238],"components,":[251],"improves":[253],"experiments,":[262],"separately":[264],"verify":[265],"our":[266,282],"method":[267],"OOD-OD":[269],"incremental":[271],"detection.":[273],"significant":[275],"performance":[276],"gains":[277],"demonstrate":[278],"superiorities":[280],"method.":[283]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":10},{"year":2024,"cited_by_count":6}],"updated_date":"2026-03-25T14:56:36.534964","created_date":"2025-10-10T00:00:00"}
