{"id":"https://openalex.org/W2965328380","doi":"https://doi.org/10.1109/tmm.2019.2928494","title":"Pay Attention to the Activations: A Modular Attention Mechanism for Fine-Grained Image Recognition","display_name":"Pay Attention to the Activations: A Modular Attention Mechanism for Fine-Grained Image Recognition","publication_year":2019,"publication_date":"2019-07-15","ids":{"openalex":"https://openalex.org/W2965328380","doi":"https://doi.org/10.1109/tmm.2019.2928494","mag":"2965328380"},"language":"en","primary_location":{"id":"doi:10.1109/tmm.2019.2928494","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tmm.2019.2928494","pdf_url":null,"source":{"id":"https://openalex.org/S137030581","display_name":"IEEE Transactions on Multimedia","issn_l":"1520-9210","issn":["1520-9210","1941-0077"],"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 Multimedia","raw_type":"journal-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1907.13075","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Pau Rodriguez","orcid":"https://orcid.org/0000-0002-1689-8084"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pau Rodriguez","raw_affiliation_strings":["Element AI, Montreal, Canada"],"raw_orcid":"https://orcid.org/0000-0002-1689-8084","affiliations":[{"raw_affiliation_string":"Element AI, Montreal, Canada","institution_ids":[]}]},{"author_position":"middle","author":{"id":null,"display_name":"Diego Velazquez","orcid":"https://orcid.org/0000-0001-6782-4336"},"institutions":[{"id":"https://openalex.org/I4210122226","display_name":"Centre de Recerca Matem\u00e0tica","ror":"https://ror.org/020s51w82","country_code":"ES","type":"facility","lineage":["https://openalex.org/I4210122226","https://openalex.org/I4387153040"]},{"id":"https://openalex.org/I4387153096","display_name":"Computer Vision Center","ror":"https://ror.org/00s0nnj93","country_code":null,"type":"other","lineage":["https://openalex.org/I4387153040","https://openalex.org/I4387153096"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"Diego Velazquez","raw_affiliation_strings":["Computer Vision Center, Bellaterra, Spain"],"raw_orcid":"https://orcid.org/0000-0001-6782-4336","affiliations":[{"raw_affiliation_string":"Computer Vision Center, Bellaterra, Spain","institution_ids":["https://openalex.org/I4210122226","https://openalex.org/I4387153096"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Guillem Cucurull","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Guillem Cucurull","raw_affiliation_strings":["Element AI, Montreal, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Element AI, Montreal, Canada","institution_ids":[]}]},{"author_position":"middle","author":{"id":null,"display_name":"Josep M. Gonfaus","orcid":null},"institutions":[{"id":"https://openalex.org/I123044942","display_name":"Universitat Aut\u00f2noma de Barcelona","ror":"https://ror.org/052g8jq94","country_code":"ES","type":"education","lineage":["https://openalex.org/I123044942"]},{"id":"https://openalex.org/I4210122226","display_name":"Centre de Recerca Matem\u00e0tica","ror":"https://ror.org/020s51w82","country_code":"ES","type":"facility","lineage":["https://openalex.org/I4210122226","https://openalex.org/I4387153040"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"Josep M. Gonfaus","raw_affiliation_strings":["Visual Tagging Services, Parc de Recerca, University Aut\u00f2noma de Barcelona, Bellaterra, Spain"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Visual Tagging Services, Parc de Recerca, University Aut\u00f2noma de Barcelona, Bellaterra, Spain","institution_ids":["https://openalex.org/I4210122226","https://openalex.org/I123044942"]}]},{"author_position":"middle","author":{"id":null,"display_name":"F. Xavier Roca","orcid":"https://orcid.org/0000-0002-7043-7334"},"institutions":[{"id":"https://openalex.org/I4210122226","display_name":"Centre de Recerca Matem\u00e0tica","ror":"https://ror.org/020s51w82","country_code":"ES","type":"facility","lineage":["https://openalex.org/I4210122226","https://openalex.org/I4387153040"]},{"id":"https://openalex.org/I4387153096","display_name":"Computer Vision Center","ror":"https://ror.org/00s0nnj93","country_code":null,"type":"other","lineage":["https://openalex.org/I4387153040","https://openalex.org/I4387153096"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"F. Xavier Roca","raw_affiliation_strings":["Computer Vision Center, Bellaterra, Spain"],"raw_orcid":"https://orcid.org/0000-0002-7043-7334","affiliations":[{"raw_affiliation_string":"Computer Vision Center, Bellaterra, Spain","institution_ids":["https://openalex.org/I4210122226","https://openalex.org/I4387153096"]}]},{"author_position":"last","author":{"id":null,"display_name":"Jordi Gonzalez","orcid":"https://orcid.org/0000-0001-8033-0306"},"institutions":[{"id":"https://openalex.org/I4210122226","display_name":"Centre de Recerca Matem\u00e0tica","ror":"https://ror.org/020s51w82","country_code":"ES","type":"facility","lineage":["https://openalex.org/I4210122226","https://openalex.org/I4387153040"]},{"id":"https://openalex.org/I4387153096","display_name":"Computer Vision Center","ror":"https://ror.org/00s0nnj93","country_code":null,"type":"other","lineage":["https://openalex.org/I4387153040","https://openalex.org/I4387153096"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"Jordi Gonzalez","raw_affiliation_strings":["Computer Vision Center, Bellaterra, Spain"],"raw_orcid":"https://orcid.org/0000-0001-8033-0306","affiliations":[{"raw_affiliation_string":"Computer Vision Center, Bellaterra, Spain","institution_ids":["https://openalex.org/I4210122226","https://openalex.org/I4387153096"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":4.0694,"has_fulltext":false,"cited_by_count":90,"citation_normalized_percentile":{"value":0.95246611,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":100},"biblio":{"volume":"22","issue":"2","first_page":"502","last_page":"514"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.43160000443458557,"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":0.43160000443458557,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.1071000024676323,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.10180000215768814,"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":0.7912999987602234},{"id":"https://openalex.org/keywords/modular-design","display_name":"Modular design","score":0.710099995136261},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6434000134468079},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5738999843597412},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5371999740600586},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.5090000033378601},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.506600022315979},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.5030999779701233},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.503000020980835}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.878000020980835},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.7912999987602234},{"id":"https://openalex.org/C101468663","wikidata":"https://www.wikidata.org/wiki/Q1620158","display_name":"Modular design","level":2,"score":0.710099995136261},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6610999703407288},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6434000134468079},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5738999843597412},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5371999740600586},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.5090000033378601},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.506600022315979},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.5030999779701233},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.503000020980835},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4625000059604645},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4350999891757965},{"id":"https://openalex.org/C2781121602","wikidata":"https://www.wikidata.org/wiki/Q3504403","display_name":"Modular neural network","level":4,"score":0.4122999906539917},{"id":"https://openalex.org/C89611455","wikidata":"https://www.wikidata.org/wiki/Q6804646","display_name":"Mechanism (biology)","level":2,"score":0.397599995136261},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.3824999928474426},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.32829999923706055},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.30869999527931213},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.30550000071525574},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.30070000886917114},{"id":"https://openalex.org/C64876066","wikidata":"https://www.wikidata.org/wiki/Q5141226","display_name":"Cognitive neuroscience of visual object recognition","level":3,"score":0.29980000853538513},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.2971999943256378},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.2939000129699707},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.28209999203681946},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.27300000190734863},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.26100000739097595},{"id":"https://openalex.org/C2778915421","wikidata":"https://www.wikidata.org/wiki/Q3643177","display_name":"Performance improvement","level":2,"score":0.26010000705718994}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/tmm.2019.2928494","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tmm.2019.2928494","pdf_url":null,"source":{"id":"https://openalex.org/S137030581","display_name":"IEEE Transactions on Multimedia","issn_l":"1520-9210","issn":["1520-9210","1941-0077"],"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 Multimedia","raw_type":"journal-article"},{"id":"pmh:oai:arXiv.org:1907.13075","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1907.13075","pdf_url":"https://arxiv.org/pdf/1907.13075","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"pmh:oai:ddd.uab.cat:326625","is_oa":true,"landing_page_url":"https://ddd.uab.cat/record/326625","pdf_url":"https://ddd.uab.cat/pub/artpub/2020/326625/IEEETranMul_a2020v22n2p502iENG.pdf","source":{"id":"https://openalex.org/S4306400381","display_name":"Dip\u00f2sit Digital de Documents de la UAB (Universitat Aut\u00f2noma de Barcelona)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I123044942","host_organization_name":"Universitat Aut\u00f2noma de Barcelona","host_organization_lineage":["https://openalex.org/I123044942"],"host_organization_lineage_names":[],"type":"repository"},"license":"public-domain","license_id":"https://openalex.org/licenses/public-domain","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1907.13075","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1907.13075","pdf_url":"https://arxiv.org/pdf/1907.13075","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":72,"referenced_works":["https://openalex.org/W1489116628","https://openalex.org/W1677182931","https://openalex.org/W1923211482","https://openalex.org/W1964856489","https://openalex.org/W1965804146","https://openalex.org/W1997618726","https://openalex.org/W2013474298","https://openalex.org/W2056238658","https://openalex.org/W2064675550","https://openalex.org/W2075970274","https://openalex.org/W2080541453","https://openalex.org/W2088049833","https://openalex.org/W2104657103","https://openalex.org/W2113172355","https://openalex.org/W2115812046","https://openalex.org/W2117539524","https://openalex.org/W2128272608","https://openalex.org/W2133946027","https://openalex.org/W2138011018","https://openalex.org/W2138068405","https://openalex.org/W2154071538","https://openalex.org/W2160039196","https://openalex.org/W2164364459","https://openalex.org/W2194775991","https://openalex.org/W2239239723","https://openalex.org/W2313077179","https://openalex.org/W2342543219","https://openalex.org/W2460852148","https://openalex.org/W2549139847","https://openalex.org/W2550553598","https://openalex.org/W2562417371","https://openalex.org/W2562731582","https://openalex.org/W2618530766","https://openalex.org/W2731519763","https://openalex.org/W2737725206","https://openalex.org/W2739107216","https://openalex.org/W2754689878","https://openalex.org/W2765268259","https://openalex.org/W2773003563","https://openalex.org/W2791779647","https://openalex.org/W2883805248","https://openalex.org/W2887057599","https://openalex.org/W2906314281","https://openalex.org/W2913950831","https://openalex.org/W2963323244","https://openalex.org/W2963446712","https://openalex.org/W2963495494","https://openalex.org/W3137241515","https://openalex.org/W6602324145","https://openalex.org/W6618372016","https://openalex.org/W6637373629","https://openalex.org/W6637556369","https://openalex.org/W6638319203","https://openalex.org/W6638641841","https://openalex.org/W6640376812","https://openalex.org/W6667268662","https://openalex.org/W6682137061","https://openalex.org/W6684665197","https://openalex.org/W6684983439","https://openalex.org/W6701498492","https://openalex.org/W6713132643","https://openalex.org/W6720905350","https://openalex.org/W6725236325","https://openalex.org/W6727947641","https://openalex.org/W6736057607","https://openalex.org/W6739901393","https://openalex.org/W6748666111","https://openalex.org/W6752285806","https://openalex.org/W6753179690","https://openalex.org/W6754046869","https://openalex.org/W6754854709","https://openalex.org/W6787972765"],"related_works":[],"abstract_inverted_index":{"Fine-grained":[0],"image":[1,87,102,274],"recognition":[2,209],"is":[3,41,97,144],"central":[4],"to":[5,44,66,105,119,121,133,183,190,287],"many":[6],"multimedia":[7],"tasks":[8,17],"such":[9,162],"as":[10,163,254],"search,":[11],"retrieval,":[12],"and":[13,49,129,135,147,154,167,179,187,189,213,224],"captioning.":[14],"Unfortunately,":[15],"these":[16],"are":[18],"still":[19],"challenging":[20],"since":[21],"the":[22,27,50,55,62,68,81,86,101,106,115,137,191,206,231,239,246,251,283],"appearance":[23],"of":[24,26,52,61,85,151,193,234,250,257],"samples":[25],"same":[28],"class":[29],"can":[30,270],"be":[31],"more":[32,181],"different":[33,37,232],"than":[34],"those":[35,131],"from":[36],"classes.":[38],"This":[39],"issue":[40],"mainly":[42],"due":[43],"changes":[45,184],"in":[46,76,93,149,185,204,219,244],"deformation,":[47],"pose,":[48],"presence":[51,192],"clutter.":[53,194],"In":[54,91,110,227],"literature,":[56],"attention":[57,96,241],"has":[58,72],"been":[59,73],"one":[60],"most":[63,82,247],"successful":[64],"strategies":[65],"handle":[67],"aforementioned":[69],"problems.":[70],"Attention":[71],"typically":[74],"implemented":[75],"neural":[77,116,268],"networks":[78,161,166],"by":[79],"selecting":[80],"informative":[83],"regions":[84,249],"that":[88,159,238,261],"improve":[89,175],"classification.":[90],"contrast,":[92],"this":[94],"paper,":[95],"not":[98],"applied":[99],"at":[100],"level":[103],"but":[104],"convolutional":[107],"feature":[108,123],"activations.":[109],"essence,":[111],"with":[112,171,262],"our":[113,172,198,235],"approach,":[114,173],"model":[117],"learns":[118],"attend":[120],"lower-level":[122],"activations":[124,132],"without":[125],"requiring":[126],"part":[127],"annotations":[128],"uses":[130],"update":[134],"rectify":[136],"output":[138],"likelihood":[139],"distribution.":[140],"The":[141],"proposed":[142,240],"mechanism":[143],"modular,":[145],"architecture-independent,":[146],"efficient":[148],"terms":[150],"both":[152],"parameters":[153],"computation":[155],"required.":[156],"Experiments":[157],"demonstrate":[158,260],"well-known":[160],"wide":[164],"residual":[165],"ResNeXt,":[168],"when":[169],"augmented":[170,267],"systematically":[174],"their":[176],"classification":[177,202],"accuracy":[178],"become":[180],"robust":[182],"deformation":[186],"pose":[188],"As":[195],"a":[196,255],"result,":[197],"proposal":[199],"reaches":[200],"state-of-the-art":[201],"accuracies":[203],"CIFAR-10,":[205],"Adience":[207],"gender":[208],"task,":[210],"Stanford":[211,225],"Dogs,":[212],"UEC-Food100":[214],"while":[215],"obtaining":[216],"competitive":[217],"performance":[218],"ImageNet,":[220],"CIFAR-100,":[221],"CUB200":[222],"Birds,":[223],"Cars.":[226],"addition,":[228],"we":[229,259],"analyze":[230],"components":[233],"model,":[236],"showing":[237],"modules":[242],"succeed":[243],"finding":[245],"discriminative":[248],"image.":[252],"Finally,":[253],"proof":[256],"concept,":[258],"only":[263],"local":[264],"predictions,":[265],"an":[266,273],"network":[269],"successfully":[271],"classify":[272],"before":[275],"reaching":[276],"any":[277],"fully":[278],"connected":[279],"layer,":[280],"thus":[281],"reducing":[282],"computational":[284],"amount":[285],"up":[286],"10%.":[288]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":17},{"year":2024,"cited_by_count":12},{"year":2023,"cited_by_count":19},{"year":2022,"cited_by_count":18},{"year":2021,"cited_by_count":15},{"year":2020,"cited_by_count":8}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2019-08-13T00:00:00"}