{"id":"https://openalex.org/W7160320727","doi":"https://doi.org/10.1109/wacv61042.2026.00625","title":"Distribution Highlighted Reference-based Label Distribution Learning for Facial Age Estimation","display_name":"Distribution Highlighted Reference-based Label Distribution Learning for Facial Age Estimation","publication_year":2026,"publication_date":"2026-03-06","ids":{"openalex":"https://openalex.org/W7160320727","doi":"https://doi.org/10.1109/wacv61042.2026.00625"},"language":null,"primary_location":{"id":"doi:10.1109/wacv61042.2026.00625","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wacv61042.2026.00625","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","raw_type":"proceedings-article"},"type":"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/A5135371012","display_name":"Satoshi Suzuki","orcid":null},"institutions":[{"id":"https://openalex.org/I2251713219","display_name":"NTT (Japan)","ror":"https://ror.org/00berct97","country_code":"JP","type":"company","lineage":["https://openalex.org/I2251713219"]},{"id":"https://openalex.org/I2801547476","display_name":"NTT Medical Center","ror":"https://ror.org/0285prp25","country_code":"JP","type":"healthcare","lineage":["https://openalex.org/I2801547476"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Satoshi Suzuki","raw_affiliation_strings":["NTT, Inc.,Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"NTT, Inc.,Japan","institution_ids":["https://openalex.org/I2251713219","https://openalex.org/I2801547476"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5081715626","display_name":"Shin\u2019ya Yamaguchi","orcid":"https://orcid.org/0000-0001-9113-7405"},"institutions":[{"id":"https://openalex.org/I2251713219","display_name":"NTT (Japan)","ror":"https://ror.org/00berct97","country_code":"JP","type":"company","lineage":["https://openalex.org/I2251713219"]},{"id":"https://openalex.org/I2801547476","display_name":"NTT Medical Center","ror":"https://ror.org/0285prp25","country_code":"JP","type":"healthcare","lineage":["https://openalex.org/I2801547476"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Shin\u2019Ya Yamaguchi","raw_affiliation_strings":["NTT, Inc.,Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"NTT, Inc.,Japan","institution_ids":["https://openalex.org/I2251713219","https://openalex.org/I2801547476"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006634833","display_name":"Shoichiro Takeda","orcid":"https://orcid.org/0000-0002-7138-4983"},"institutions":[{"id":"https://openalex.org/I2251713219","display_name":"NTT (Japan)","ror":"https://ror.org/00berct97","country_code":"JP","type":"company","lineage":["https://openalex.org/I2251713219"]},{"id":"https://openalex.org/I2801547476","display_name":"NTT Medical Center","ror":"https://ror.org/0285prp25","country_code":"JP","type":"healthcare","lineage":["https://openalex.org/I2801547476"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Shoichiro Takeda","raw_affiliation_strings":["NTT, Inc.,Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"NTT, Inc.,Japan","institution_ids":["https://openalex.org/I2251713219","https://openalex.org/I2801547476"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5020693766","display_name":"Takuhiro Kaneko","orcid":"https://orcid.org/0009-0000-8016-5144"},"institutions":[{"id":"https://openalex.org/I2251713219","display_name":"NTT (Japan)","ror":"https://ror.org/00berct97","country_code":"JP","type":"company","lineage":["https://openalex.org/I2251713219"]},{"id":"https://openalex.org/I2801547476","display_name":"NTT Medical Center","ror":"https://ror.org/0285prp25","country_code":"JP","type":"healthcare","lineage":["https://openalex.org/I2801547476"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Takuhiro Kaneko","raw_affiliation_strings":["NTT, Inc.,Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"NTT, Inc.,Japan","institution_ids":["https://openalex.org/I2251713219","https://openalex.org/I2801547476"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5013009514","display_name":"Shota Orihashi","orcid":null},"institutions":[{"id":"https://openalex.org/I2251713219","display_name":"NTT (Japan)","ror":"https://ror.org/00berct97","country_code":"JP","type":"company","lineage":["https://openalex.org/I2251713219"]},{"id":"https://openalex.org/I2801547476","display_name":"NTT Medical Center","ror":"https://ror.org/0285prp25","country_code":"JP","type":"healthcare","lineage":["https://openalex.org/I2801547476"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Shota Orihashi","raw_affiliation_strings":["NTT, Inc.,Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"NTT, Inc.,Japan","institution_ids":["https://openalex.org/I2251713219","https://openalex.org/I2801547476"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5060644399","display_name":"Ryo Masumura","orcid":"https://orcid.org/0000-0002-2415-4149"},"institutions":[{"id":"https://openalex.org/I2251713219","display_name":"NTT (Japan)","ror":"https://ror.org/00berct97","country_code":"JP","type":"company","lineage":["https://openalex.org/I2251713219"]},{"id":"https://openalex.org/I2801547476","display_name":"NTT Medical Center","ror":"https://ror.org/0285prp25","country_code":"JP","type":"healthcare","lineage":["https://openalex.org/I2801547476"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Ryo Masumura","raw_affiliation_strings":["NTT, Inc.,Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"NTT, Inc.,Japan","institution_ids":["https://openalex.org/I2251713219","https://openalex.org/I2801547476"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5135371012"],"corresponding_institution_ids":["https://openalex.org/I2251713219","https://openalex.org/I2801547476"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.93440106,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"6464","last_page":"6473"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11448","display_name":"Face recognition and analysis","score":0.9760000109672546,"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/T11448","display_name":"Face recognition and analysis","score":0.9760000109672546,"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/T12301","display_name":"Facial Nerve Paralysis Treatment and Research","score":0.004800000227987766,"subfield":{"id":"https://openalex.org/subfields/2728","display_name":"Neurology"},"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/T10057","display_name":"Face and Expression Recognition","score":0.00430000014603138,"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/distribution","display_name":"Distribution (mathematics)","score":0.550599992275238},{"id":"https://openalex.org/keywords/estimation","display_name":"Estimation","score":0.47269999980926514},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.44449999928474426},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.32499998807907104},{"id":"https://openalex.org/keywords/face","display_name":"Face (sociological concept)","score":0.30720001459121704}],"concepts":[{"id":"https://openalex.org/C110121322","wikidata":"https://www.wikidata.org/wiki/Q865811","display_name":"Distribution (mathematics)","level":2,"score":0.550599992275238},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5357999801635742},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.47450000047683716},{"id":"https://openalex.org/C96250715","wikidata":"https://www.wikidata.org/wiki/Q965330","display_name":"Estimation","level":2,"score":0.47269999980926514},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.4462999999523163},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.44449999928474426},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.43309998512268066},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.32499998807907104},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.30720001459121704},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.26759999990463257},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.25609999895095825},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.25540000200271606},{"id":"https://openalex.org/C49232408","wikidata":"https://www.wikidata.org/wiki/Q576072","display_name":"Student's t-distribution","level":4,"score":0.2515000104904175}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/wacv61042.2026.00625","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wacv61042.2026.00625","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":38,"referenced_works":["https://openalex.org/W2009088607","https://openalex.org/W2103077782","https://openalex.org/W2115651492","https://openalex.org/W2118664399","https://openalex.org/W2164715565","https://openalex.org/W2194775991","https://openalex.org/W2239239723","https://openalex.org/W2330485005","https://openalex.org/W2440214111","https://openalex.org/W2510725918","https://openalex.org/W2553156677","https://openalex.org/W2592232824","https://openalex.org/W2771116199","https://openalex.org/W2798868324","https://openalex.org/W2807904173","https://openalex.org/W2949208911","https://openalex.org/W2955216108","https://openalex.org/W2962851632","https://openalex.org/W2963785012","https://openalex.org/W2963802733","https://openalex.org/W2963980515","https://openalex.org/W3034552680","https://openalex.org/W3035714233","https://openalex.org/W3037164804","https://openalex.org/W3042241776","https://openalex.org/W3089831171","https://openalex.org/W3096840866","https://openalex.org/W3097760447","https://openalex.org/W3106701815","https://openalex.org/W3177357839","https://openalex.org/W4312501338","https://openalex.org/W4312771329","https://openalex.org/W4382458595","https://openalex.org/W4386065360","https://openalex.org/W4391248739","https://openalex.org/W4402727470","https://openalex.org/W4404725424","https://openalex.org/W7133185641"],"related_works":[],"abstract_inverted_index":{"Estimating":[0],"age":[1,12,62],"from":[2],"facial":[3],"images":[4,146],"is":[5],"a":[6,39,45,60,82,99],"fundamental":[7],"task.":[8],"In":[9],"this":[10,32],"task,":[11],"labels":[13],"have":[14],"ambiguity":[15,126],"because":[16],"faces":[17],"of":[18,59],"the":[19,49,65,76,109,112,124,129,148,159,167,171],"same":[20],"individual":[21],"across":[22,184],"similar":[23],"ages":[24],"are":[25],"often":[26,71],"difficult":[27],"to":[28,55,73,122,147],"distinguish.":[29],"To":[30],"model":[31,75],"ambiguity,":[33],"label":[34,46,77,125],"distribution":[35,87],"learning":[36],"(LDL)":[37],"trains":[38],"deep":[40],"neural":[41],"network":[42],"(DNN)":[43],"using":[44],"distribution,":[47],"i.e.,":[48],"probability":[50],"that":[51,178],"an":[52,94,153],"image":[53],"belongs":[54],"each":[56],"age,":[57],"instead":[58],"single":[61],"label.":[63],"However,":[64],"heuristic":[66],"constraints":[67],"utilized":[68],"for":[69],"LDL":[70,84,90,105],"fail":[72],"accurately":[74],"ambiguity.":[78],"Therefore,":[79],"we":[80],"propose":[81],"novel":[83],"method":[85,106,180],"called":[86],"highlighted":[88],"reference-based":[89],"(DHRL),":[91],"which":[92],"introduces":[93],"input-dependent":[95],"constraint":[96],"by":[97,156],"utilizing":[98],"reference":[100,113,131,149],"DNN":[101,150],"pre-trained":[102,130],"with":[103],"any":[104],"and":[107,114,138,151,187],"minimizing":[108],"gap":[110,168],"between":[111,170],"target":[115],"DNNs\u2019":[116,173],"outputs.":[117,160,174],"DHRL":[118],"incorporates":[119],"two":[120,172],"techniques":[121],"highlight":[123],"hidden":[127],"in":[128,166],"DNN\u2019s":[132],"output:":[133],"noisy":[134,145],"augmentation-based":[135],"ensembling":[136],"(NAE)":[137],"different":[139],"scale":[140],"multi-temperature":[141],"(DSM).":[142],"NAE":[143],"inputs":[144],"provides":[152],"ensemble":[154],"effect":[155],"averaging":[157],"all":[158],"DSM":[161],"sets":[162],"multiple":[163],"temperatures":[164],"simultaneously":[165],"minimization":[169],"Experimental":[175],"results":[176],"indicate":[177],"our":[179],"achieves":[181],"state-of-the-art":[182],"performance":[183],"various":[185],"datasets":[186],"conditions.":[188]},"counts_by_year":[],"updated_date":"2026-05-07T06:04:25.777469","created_date":"2026-05-06T00:00:00"}