{"id":"https://openalex.org/W2902038253","doi":"https://doi.org/10.1109/icpr.2018.8545164","title":"R<sup>2</sup>-ResNeXt: A ResNeXt-Based Regression Model with Relative Ranking for Facial Beauty Prediction","display_name":"R<sup>2</sup>-ResNeXt: A ResNeXt-Based Regression Model with Relative Ranking for Facial Beauty Prediction","publication_year":2018,"publication_date":"2018-08-01","ids":{"openalex":"https://openalex.org/W2902038253","doi":"https://doi.org/10.1109/icpr.2018.8545164","mag":"2902038253"},"language":"en","primary_location":{"id":"doi:10.1109/icpr.2018.8545164","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr.2018.8545164","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 24th International Conference on Pattern Recognition (ICPR)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5010577514","display_name":"Luojun Lin","orcid":"https://orcid.org/0000-0002-1141-2487"},"institutions":[{"id":"https://openalex.org/I90610280","display_name":"South China University of Technology","ror":"https://ror.org/0530pts50","country_code":"CN","type":"education","lineage":["https://openalex.org/I90610280"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Luojun Lin","raw_affiliation_strings":["South China University of Technology, Guangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"South China University of Technology, Guangzhou, China","institution_ids":["https://openalex.org/I90610280"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5067156323","display_name":"Lingyu Liang","orcid":"https://orcid.org/0000-0003-1815-051X"},"institutions":[{"id":"https://openalex.org/I90610280","display_name":"South China University of Technology","ror":"https://ror.org/0530pts50","country_code":"CN","type":"education","lineage":["https://openalex.org/I90610280"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lingyu Liang","raw_affiliation_strings":["South China University of Technology, Guangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"South China University of Technology, Guangzhou, China","institution_ids":["https://openalex.org/I90610280"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5080674767","display_name":"Lianwen Jin","orcid":"https://orcid.org/0000-0002-5456-0957"},"institutions":[{"id":"https://openalex.org/I90610280","display_name":"South China University of Technology","ror":"https://ror.org/0530pts50","country_code":"CN","type":"education","lineage":["https://openalex.org/I90610280"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lianwen Jin","raw_affiliation_strings":["South China University of Technology, Guangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"South China University of Technology, Guangzhou, China","institution_ids":["https://openalex.org/I90610280"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I90610280"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":22,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"85","last_page":"90"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11118","display_name":"Evolutionary Psychology and Human Behavior","score":0.9950000047683716,"subfield":{"id":"https://openalex.org/subfields/3205","display_name":"Experimental and Cognitive Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T11118","display_name":"Evolutionary Psychology and Human Behavior","score":0.9950000047683716,"subfield":{"id":"https://openalex.org/subfields/3205","display_name":"Experimental and Cognitive Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11448","display_name":"Face recognition and analysis","score":0.9932000041007996,"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/T11322","display_name":"Facial Rejuvenation and Surgery Techniques","score":0.9354000091552734,"subfield":{"id":"https://openalex.org/subfields/2708","display_name":"Dermatology"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6385419368743896},{"id":"https://openalex.org/keywords/ranking","display_name":"Ranking (information retrieval)","score":0.5889625549316406},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5782174468040466},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.5115566849708557},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.5009887218475342},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.48926565051078796},{"id":"https://openalex.org/keywords/regression-analysis","display_name":"Regression analysis","score":0.4492821991443634},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.4279744625091553},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4002474546432495},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3970152735710144},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3703961968421936},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.245581716299057}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6385419368743896},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.5889625549316406},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5782174468040466},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.5115566849708557},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.5009887218475342},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.48926565051078796},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.4492821991443634},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.4279744625091553},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4002474546432495},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3970152735710144},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3703961968421936},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.245581716299057},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icpr.2018.8545164","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr.2018.8545164","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 24th International Conference on Pattern Recognition (ICPR)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities","score":0.7300000190734863}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":42,"referenced_works":["https://openalex.org/W1686810756","https://openalex.org/W1883761234","https://openalex.org/W1965870148","https://openalex.org/W1970769814","https://openalex.org/W1971375352","https://openalex.org/W1972968514","https://openalex.org/W1982469530","https://openalex.org/W1985968965","https://openalex.org/W1987208496","https://openalex.org/W1988047116","https://openalex.org/W1993741516","https://openalex.org/W2003118627","https://openalex.org/W2031365440","https://openalex.org/W2032244911","https://openalex.org/W2102836959","https://openalex.org/W2117539524","https://openalex.org/W2143761310","https://openalex.org/W2151541811","https://openalex.org/W2152186880","https://openalex.org/W2155893237","https://openalex.org/W2157364932","https://openalex.org/W2157395013","https://openalex.org/W2163605009","https://openalex.org/W2194775991","https://openalex.org/W2231999017","https://openalex.org/W2238950008","https://openalex.org/W2511250175","https://openalex.org/W2543791852","https://openalex.org/W2549139847","https://openalex.org/W2604184171","https://openalex.org/W2620075774","https://openalex.org/W2741457358","https://openalex.org/W2790393062","https://openalex.org/W2949624713","https://openalex.org/W2963125010","https://openalex.org/W2963446712","https://openalex.org/W2964118336","https://openalex.org/W6682468274","https://openalex.org/W6682525936","https://openalex.org/W6684191040","https://openalex.org/W6689559109","https://openalex.org/W6735789499"],"related_works":["https://openalex.org/W2030978506","https://openalex.org/W31220157","https://openalex.org/W2312753042","https://openalex.org/W4289356671","https://openalex.org/W2389155397","https://openalex.org/W2165884543","https://openalex.org/W3186837933","https://openalex.org/W2368989808","https://openalex.org/W1969346022","https://openalex.org/W2034959125"],"abstract_inverted_index":{"The":[0,105,176],"purpose":[1],"of":[2,23,27,36,49,68,204],"facial":[3,15,28,34,123,217],"beauty":[4,29,211,218],"prediction":[5,30,38],"(FBP)":[6],"is":[7,31,61],"to":[8,63,91,112,157],"develop":[9,139],"a":[10,18,45,77,155,159],"machine":[11],"that":[12,56,143,184],"automatically":[13],"evaluates":[14],"attractiveness":[16,124],"in":[17,126],"human":[19],"perceptual":[20],"manner.":[21,129],"One":[22],"the":[24,32,37,57,65,82,86,97,114,132,172,180,192,202,205],"essential":[25],"problem":[26],"discriminative":[33],"representation":[35,115],"model.":[39],"Previous":[40],"methods":[41],"formulate":[42],"FBP":[43],"as":[44,99],"specific":[46],"supervised":[47],"learning":[48],"classification,":[50],"regression,":[51],"or":[52],"ranking.":[53],"We":[54,152],"find":[55],"relative":[58,83,120,210],"ranking":[59,84,121,149,212],"information":[60],"useful":[62],"improve":[64],"regression":[66,78,145,214],"model":[67,79,98],"FBP.":[69],"Based":[70],"on":[71,179],"this":[72,74],"observation,":[73],"paper":[75],"proposes":[76],"guided":[80,118],"by":[81,119],"with":[85,196],"state-of-the-art":[87,193],"Res":[88],"NeXt":[89],"structure":[90],"achieve":[92],"FBP,":[93],"and":[94,116,147,199,209],"we":[95,138],"call":[96],"R":[100,106,133,186],"<sup":[101,107,134,187],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[102,108,135,188],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">2</sup>":[103,109,136,189],"-ResNeXt.":[104],"-ResNeXt":[110,190],"facilitates":[111],"learn":[113],"predictor":[117],"for":[122,216],"assessment":[125],"an":[127,140],"end-to-end":[128],"To":[130],"train":[131],"-ResNeXt,":[137],"aggregated":[141],"loss":[142,146,150],"combines":[144],"pairwise":[148],"linearly.":[151],"also":[153],"design":[154],"method":[156],"construct":[158],"dataset":[160],"containing":[161],"relatively":[162],"-labelled":[163],"image":[164],"pairs":[165],"whose":[166],"individual":[167],"images":[168],"are":[169],"sampled":[170],"from":[171],"SCUT-FBP":[173,181],"benchmark":[174,182],"database.":[175],"experimental":[177],"results":[178],"show":[183],"our":[185],"achieves":[191],"performance":[194],"compared":[195],"related":[197],"literatures,":[198],"further":[200],"indicates":[201],"effectiveness":[203],"deep":[206],"residual":[207],"architecture":[208],"into":[213],"task":[215],"prediction.":[219]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":4},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
