{"id":"https://openalex.org/W3002933382","doi":"https://doi.org/10.1109/icce-berlin47944.2019.8966159","title":"Accuracy Improvement of Fashion Style Estimation with Attention Control of a Classifier","display_name":"Accuracy Improvement of Fashion Style Estimation with Attention Control of a Classifier","publication_year":2019,"publication_date":"2019-09-08","ids":{"openalex":"https://openalex.org/W3002933382","doi":"https://doi.org/10.1109/icce-berlin47944.2019.8966159","mag":"3002933382"},"language":"en","primary_location":{"id":"doi:10.1109/icce-berlin47944.2019.8966159","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icce-berlin47944.2019.8966159","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE 9th International Conference on Consumer Electronics (ICCE-Berlin)","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/A5002456145","display_name":"Risako Aoki","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Risako Aoki","raw_affiliation_strings":["Graduate School of Science and Technology,Department of Computer Science","Department of Computer Science, Graduate School of Science and Technology"],"affiliations":[{"raw_affiliation_string":"Graduate School of Science and Technology,Department of Computer Science","institution_ids":[]},{"raw_affiliation_string":"Department of Computer Science, Graduate School of Science and Technology","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102112729","display_name":"Takeshi Nakajima","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Takeshi Nakajima","raw_affiliation_strings":["Graduate School of Science and Technology,Department of Computer Science","Department of Computer Science, Graduate School of Science and Technology"],"affiliations":[{"raw_affiliation_string":"Graduate School of Science and Technology,Department of Computer Science","institution_ids":[]},{"raw_affiliation_string":"Department of Computer Science, Graduate School of Science and Technology","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103820524","display_name":"Takuro Oki","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Takuro Oki","raw_affiliation_strings":["Graduate School of Science and Technology,Department of Computer Science","Department of Computer Science, Graduate School of Science and Technology"],"affiliations":[{"raw_affiliation_string":"Graduate School of Science and Technology,Department of Computer Science","institution_ids":[]},{"raw_affiliation_string":"Department of Computer Science, Graduate School of Science and Technology","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5035420223","display_name":"Ryusuke Miyamoto","orcid":"https://orcid.org/0000-0001-9450-4493"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ryusuke Miyamoto","raw_affiliation_strings":["Graduate School of Science and Technology,Department of Computer Science","Department of Computer Science, Graduate School of Science and Technology"],"affiliations":[{"raw_affiliation_string":"Graduate School of Science and Technology,Department of Computer Science","institution_ids":[]},{"raw_affiliation_string":"Department of Computer Science, Graduate School of Science and Technology","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5002456145"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.2024,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.56367005,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"289","last_page":"294"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.9973000288009644,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.9973000288009644,"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":0.9957000017166138,"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/T11605","display_name":"Visual Attention and Saliency Detection","score":0.9876000285148621,"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/classifier","display_name":"Classifier (UML)","score":0.7945277690887451},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7688928842544556},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7618168592453003},{"id":"https://openalex.org/keywords/extractor","display_name":"Extractor","score":0.7057228088378906},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.683962881565094},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.5860398411750793},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.57956862449646},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5540030002593994},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5466958284378052},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.45314866304397583},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.09749901294708252}],"concepts":[{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.7945277690887451},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7688928842544556},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7618168592453003},{"id":"https://openalex.org/C117978034","wikidata":"https://www.wikidata.org/wiki/Q5422192","display_name":"Extractor","level":2,"score":0.7057228088378906},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.683962881565094},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.5860398411750793},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.57956862449646},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5540030002593994},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5466958284378052},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.45314866304397583},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.09749901294708252},{"id":"https://openalex.org/C21880701","wikidata":"https://www.wikidata.org/wiki/Q2144042","display_name":"Process engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icce-berlin47944.2019.8966159","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icce-berlin47944.2019.8966159","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE 9th International Conference on Consumer Electronics (ICCE-Berlin)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":32,"referenced_works":["https://openalex.org/W181871703","https://openalex.org/W1686810756","https://openalex.org/W2037227137","https://openalex.org/W2038147967","https://openalex.org/W2097117768","https://openalex.org/W2117539524","https://openalex.org/W2163605009","https://openalex.org/W2194775991","https://openalex.org/W2395611524","https://openalex.org/W2463470988","https://openalex.org/W2560023338","https://openalex.org/W2593649459","https://openalex.org/W2602877373","https://openalex.org/W2604272474","https://openalex.org/W2614562328","https://openalex.org/W2765683101","https://openalex.org/W2769033722","https://openalex.org/W2770929279","https://openalex.org/W2943009500","https://openalex.org/W2962835968","https://openalex.org/W2963367015","https://openalex.org/W2963483334","https://openalex.org/W2964217532","https://openalex.org/W3098379913","https://openalex.org/W3106250896","https://openalex.org/W4298125807","https://openalex.org/W6637373629","https://openalex.org/W6734531890","https://openalex.org/W6735585068","https://openalex.org/W6737324727","https://openalex.org/W6739258409","https://openalex.org/W6785652829"],"related_works":["https://openalex.org/W3082848404","https://openalex.org/W1979583797","https://openalex.org/W2016864125","https://openalex.org/W4293226380","https://openalex.org/W2090763504","https://openalex.org/W3141979996","https://openalex.org/W2945706271","https://openalex.org/W4387435415","https://openalex.org/W2114169842","https://openalex.org/W2535808783"],"abstract_inverted_index":{"Computer":[0],"vision":[1],"has":[2],"begun":[3],"to":[4,7,11,41,46,97,107],"be":[5,165],"applied":[6],"fashion":[8,51],"applications":[9],"owing":[10],"the":[12,25,48,56,60,75,84,93,104,154,169,172],"remarkable":[13],"development":[14],"of":[15,27,50,62,77,130,151,171],"image":[16],"recognition":[17],"technology":[18],"and":[19,39,82,103,123,133],"deep":[20],"learning.":[21],"Fashion":[22],"style":[23,52],"estimation,":[24],"focus":[26],"this":[28],"paper,":[29],"is":[30,174],"a":[31,63,99,109,148],"hot":[32],"topic":[33],"as":[34],"it":[35],"provides":[36],"useful":[37],"information":[38],"services":[40],"consumers.":[42],"This":[43],"paper":[44],"aims":[45],"enhance":[47],"accuracy":[49,150,163],"estimation":[53],"by":[54],"improving":[55,78],"classifier.":[57],"To":[58],"control":[59],"attention":[61,86,170],"classifier,":[64],"two":[65],"different":[66],"approaches":[67],"are":[68],"followed:":[69],"human":[70],"detection":[71,116],"after":[72,117],"segmentation":[73],"with":[74,121,125,140],"aim":[76],"on":[79],"previous":[80],"schemes":[81],"enhancing":[83],"visual":[85],"model":[87],"(VAM).":[88],"Experiments":[89],"were":[90],"conducted":[91],"using":[92,114],"Hipster":[94],"Wars":[95],"dataset":[96,106],"train":[98],"final":[100],"SVM-based":[101],"classifier":[102,173],"WEARStyle":[105],"construct":[108],"feature":[110],"extractor.":[111],"The":[112,158],"results":[113,159],"SSD-based":[115],"PSPNet-based":[118],"segmentation,":[119],"VAM":[120,124,139],"ICNet,":[122],"PSPNet":[126],"showed":[127],"classification":[128,149,162],"accuracies":[129],"83.8%,":[131],"84.7%,":[132],"85.1%,":[134],"respectively.":[135],"All":[136],"these":[137],"outperformed":[138],"full":[141],"convolutional":[142],"network":[143],"(FCN),":[144],"which":[145],"resulted":[146],"in":[147],"83.0%":[152],"under":[153],"same":[155],"experimental":[156],"conditions.":[157],"indicate":[160],"that":[161],"can":[164],"improved":[166],"upon":[167],"when":[168],"controlled.":[175]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2020,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
