{"id":"https://openalex.org/W3214871013","doi":"https://doi.org/10.1109/tip.2021.3128318","title":"Divergent Angular Representation for Open Set Image Recognition","display_name":"Divergent Angular Representation for Open Set Image Recognition","publication_year":2021,"publication_date":"2021-11-25","ids":{"openalex":"https://openalex.org/W3214871013","doi":"https://doi.org/10.1109/tip.2021.3128318","mag":"3214871013","pmid":"https://pubmed.ncbi.nlm.nih.gov/34822329"},"language":"en","primary_location":{"id":"doi:10.1109/tip.2021.3128318","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tip.2021.3128318","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":null,"display_name":"Jaewoo Park","orcid":"https://orcid.org/0000-0001-7508-3371"},"institutions":[{"id":"https://openalex.org/I193775966","display_name":"Yonsei University","ror":"https://ror.org/01wjejq96","country_code":"KR","type":"education","lineage":["https://openalex.org/I193775966"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Jaewoo Park","raw_affiliation_strings":["School of Electrical and Electronic Engineering, College of Engineering, Yonsei University, Seoul, South Korea"],"raw_orcid":"https://orcid.org/0000-0001-7508-3371","affiliations":[{"raw_affiliation_string":"School of Electrical and Electronic Engineering, College of Engineering, Yonsei University, Seoul, South Korea","institution_ids":["https://openalex.org/I193775966"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Cheng Yaw Low","orcid":"https://orcid.org/0000-0002-6764-0614"},"institutions":[{"id":"https://openalex.org/I193775966","display_name":"Yonsei University","ror":"https://ror.org/01wjejq96","country_code":"KR","type":"education","lineage":["https://openalex.org/I193775966"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Cheng Yaw Low","raw_affiliation_strings":["School of Electrical and Electronic Engineering, College of Engineering, Yonsei University, Seoul, South Korea"],"raw_orcid":"https://orcid.org/0000-0002-6764-0614","affiliations":[{"raw_affiliation_string":"School of Electrical and Electronic Engineering, College of Engineering, Yonsei University, Seoul, South Korea","institution_ids":["https://openalex.org/I193775966"]}]},{"author_position":"last","author":{"id":null,"display_name":"Andrew Beng Jin Teoh","orcid":"https://orcid.org/0000-0001-5063-9484"},"institutions":[{"id":"https://openalex.org/I193775966","display_name":"Yonsei University","ror":"https://ror.org/01wjejq96","country_code":"KR","type":"education","lineage":["https://openalex.org/I193775966"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Andrew Beng Jin Teoh","raw_affiliation_strings":["School of Electrical and Electronic Engineering, College of Engineering, Yonsei University, Seoul, South Korea"],"raw_orcid":"https://orcid.org/0000-0001-5063-9484","affiliations":[{"raw_affiliation_string":"School of Electrical and Electronic Engineering, College of Engineering, Yonsei University, Seoul, South Korea","institution_ids":["https://openalex.org/I193775966"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I193775966"],"apc_list":null,"apc_paid":null,"fwci":0.6824,"has_fulltext":false,"cited_by_count":10,"citation_normalized_percentile":{"value":0.76109074,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":"31","issue":null,"first_page":"176","last_page":"189"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.8622000217437744,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.8622000217437744,"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/T10057","display_name":"Face and Expression Recognition","score":0.04390000179409981,"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/T11448","display_name":"Face recognition and analysis","score":0.010300000198185444,"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/discriminative-model","display_name":"Discriminative model","score":0.8567000031471252},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.7373999953269958},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6876999735832214},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.5253999829292297},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5103999972343445},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.4950000047683716},{"id":"https://openalex.org/keywords/open-set","display_name":"Open set","score":0.4927999973297119},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.48739999532699585}],"concepts":[{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.8567000031471252},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.7373999953269958},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7207000255584717},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6876999735832214},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.5253999829292297},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5103999972343445},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.4950000047683716},{"id":"https://openalex.org/C42357961","wikidata":"https://www.wikidata.org/wiki/Q213363","display_name":"Open set","level":2,"score":0.4927999973297119},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.48739999532699585},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4542999863624573},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.38420000672340393},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.376800000667572},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.375900000333786},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.3467999994754791},{"id":"https://openalex.org/C64876066","wikidata":"https://www.wikidata.org/wiki/Q5141226","display_name":"Cognitive neuroscience of visual object recognition","level":3,"score":0.3000999987125397},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2922999858856201},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.29109999537467957},{"id":"https://openalex.org/C143271835","wikidata":"https://www.wikidata.org/wiki/Q254515","display_name":"Similitude","level":2,"score":0.2851000130176544},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.25769999623298645},{"id":"https://openalex.org/C2778334786","wikidata":"https://www.wikidata.org/wiki/Q1586270","display_name":"Variation (astronomy)","level":2,"score":0.2533999979496002},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.25270000100135803}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tip.2021.3128318","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tip.2021.3128318","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:34822329","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/34822329","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/G7238793834","display_name":null,"funder_award_id":"NRF-2019R1A2C1003306","funder_id":"https://openalex.org/F4320322120","funder_display_name":"National Research Foundation of Korea"}],"funders":[{"id":"https://openalex.org/F4320322120","display_name":"National Research Foundation of Korea","ror":"https://ror.org/013aysd81"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":49,"referenced_works":["https://openalex.org/W572355794","https://openalex.org/W1916279783","https://openalex.org/W1970088130","https://openalex.org/W2004211009","https://openalex.org/W2018459374","https://openalex.org/W2051224630","https://openalex.org/W2105497548","https://openalex.org/W2194775991","https://openalex.org/W2418213296","https://openalex.org/W2418633638","https://openalex.org/W2466114631","https://openalex.org/W2783748519","https://openalex.org/W2787720882","https://openalex.org/W2895752198","https://openalex.org/W2901114541","https://openalex.org/W2904509905","https://openalex.org/W2925312408","https://openalex.org/W2962856082","https://openalex.org/W2963049059","https://openalex.org/W2963149653","https://openalex.org/W2963285706","https://openalex.org/W2963875483","https://openalex.org/W2963924212","https://openalex.org/W2964137095","https://openalex.org/W2973218493","https://openalex.org/W3035224069","https://openalex.org/W3102762626","https://openalex.org/W3112288498","https://openalex.org/W3113047382","https://openalex.org/W3123566204","https://openalex.org/W4205598956","https://openalex.org/W6631190155","https://openalex.org/W6637373629","https://openalex.org/W6637568146","https://openalex.org/W6640425456","https://openalex.org/W6684191040","https://openalex.org/W6703116779","https://openalex.org/W6726497184","https://openalex.org/W6728622933","https://openalex.org/W6743688258","https://openalex.org/W6745553787","https://openalex.org/W6745891213","https://openalex.org/W6751494907","https://openalex.org/W6751866786","https://openalex.org/W6752760542","https://openalex.org/W6765696844","https://openalex.org/W6771795890","https://openalex.org/W6787972765","https://openalex.org/W6791148617"],"related_works":[],"abstract_inverted_index":{"Open":[0],"set":[1],"recognition":[2],"(OSR)":[3],"models":[4],"need":[5],"not":[6],"only":[7],"discriminate":[8],"between":[9,96,108],"known":[10,30,47,53,73,97,109],"classes":[11,31,48,98,112],"but":[12],"also":[13],"detect":[14],"unknown":[15,55,68,111],"class":[16,42,69,74],"samples":[17],"unavailable":[18],"during":[19],"training.":[20],"One":[21],"promising":[22],"approach":[23],"is":[24,130],"to":[25,52,71,105,132,140],"learn":[26,64],"discriminative":[27,102],"representations":[28,65,70,123],"over":[29,124],"with":[32],"strong":[33],"intra-class":[34],"similarity":[35],"and":[36,54,110,137,157,172,179],"inter-class":[37],"discrepancy.":[38],"Then,":[39],"the":[40,46,61,72,114,119,134,170],"powerful":[41],"discrimination":[43,95],"learned":[44],"from":[45],"can":[49],"be":[50],"extended":[51],"classes.":[56],"Without":[57],"appropriate":[58],"regularization,":[59],"however,":[60],"model":[62],"may":[63],"trivially,":[66],"collapsing":[67],"ones.":[75],"To":[76],"resolve":[77],"this":[78],"problem,":[79],"we":[80],"propose":[81],"Divergent":[82],"Angular":[83],"Representation":[84],"(DivAR)":[85],"based":[86],"on":[87,165],"two":[88],"approaches.":[89],"Firstly,":[90],"DivAR":[91,117,139,155,175],"maximizes":[92],"its":[93],"representational":[94],"via":[99],"a":[100,160],"highly":[101],"loss.":[103],"Secondly,":[104],"ensure":[106],"separation":[107],"in":[113,159],"representation":[115],"space,":[116],"boosts":[118],"directional":[120],"variation":[121],"of":[122,174],"global":[125],"samples.":[126],"In":[127],"addition,":[128],"self-supervision":[129],"leveraged":[131],"improve":[133],"representation's":[135],"robustness":[136],"extend":[138],"one-class":[141],"classification.":[142],"Moreover,":[143],"unlike":[144],"other":[145],"OSR":[146,178],"methods":[147],"that":[148],"require":[149],"an":[150],"extra":[151],"machinery":[152],"for":[153,176],"inference,":[154],"learns":[156],"infers":[158],"single":[161],"module.":[162],"Extensive":[163],"experiments":[164],"generic":[166],"image":[167],"datasets":[168],"demonstrate":[169],"plausibility":[171],"effectiveness":[173],"both":[177],"One-Class":[180],"Classification":[181],"(OCC)":[182],"problems.":[183]},"counts_by_year":[{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":4}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2021-12-06T00:00:00"}
