{"id":"https://openalex.org/W2972850892","doi":"https://doi.org/10.1145/3343031.3351062","title":"PDANet","display_name":"PDANet","publication_year":2019,"publication_date":"2019-10-15","ids":{"openalex":"https://openalex.org/W2972850892","doi":"https://doi.org/10.1145/3343031.3351062","mag":"2972850892"},"language":"en","primary_location":{"id":"doi:10.1145/3343031.3351062","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3343031.3351062","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 27th ACM International Conference on Multimedia","raw_type":"proceedings-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1909.05693","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Sicheng Zhao","orcid":null},"institutions":[{"id":"https://openalex.org/I95457486","display_name":"University of California, Berkeley","ror":"https://ror.org/01an7q238","country_code":"US","type":"education","lineage":["https://openalex.org/I95457486"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Sicheng Zhao","raw_affiliation_strings":["University of California, Berkeley, Berkeley, CA, USA"],"affiliations":[{"raw_affiliation_string":"University of California, Berkeley, Berkeley, CA, USA","institution_ids":["https://openalex.org/I95457486"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Zizhou Jia","orcid":null},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zizhou Jia","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Hui Chen","orcid":null},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hui Chen","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Leida Li","orcid":null},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Leida Li","raw_affiliation_strings":["Xidian University, Xi'an, China"],"affiliations":[{"raw_affiliation_string":"Xidian University, Xi'an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Guiguang Ding","orcid":null},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guiguang Ding","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"last","author":{"id":null,"display_name":"Kurt Keutzer","orcid":null},"institutions":[{"id":"https://openalex.org/I95457486","display_name":"University of California, Berkeley","ror":"https://ror.org/01an7q238","country_code":"US","type":"education","lineage":["https://openalex.org/I95457486"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kurt Keutzer","raw_affiliation_strings":["University of California, Berkeley, Berkeley, CA, USA"],"affiliations":[{"raw_affiliation_string":"University of California, Berkeley, Berkeley, CA, USA","institution_ids":["https://openalex.org/I95457486"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":6,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I95457486"],"apc_list":null,"apc_paid":null,"fwci":41.6114,"has_fulltext":false,"cited_by_count":77,"citation_normalized_percentile":{"value":0.99726754,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"192","last_page":"201"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13731","display_name":"Advanced Computing and Algorithms","score":0.9929999709129333,"subfield":{"id":"https://openalex.org/subfields/3322","display_name":"Urban Studies"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T13731","display_name":"Advanced Computing and Algorithms","score":0.9929999709129333,"subfield":{"id":"https://openalex.org/subfields/3322","display_name":"Urban Studies"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11605","display_name":"Visual Attention and Saliency Detection","score":0.9923999905586243,"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/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.9749000072479248,"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/convolutional-neural-network","display_name":"Convolutional neural network","score":0.678600013256073},{"id":"https://openalex.org/keywords/margin","display_name":"Margin (machine learning)","score":0.6061999797821045},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.5422000288963318},{"id":"https://openalex.org/keywords/polarity","display_name":"Polarity (international relations)","score":0.5414000153541565},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.4584999978542328},{"id":"https://openalex.org/keywords/visualization","display_name":"Visualization","score":0.4374000132083893},{"id":"https://openalex.org/keywords/emotion-classification","display_name":"Emotion classification","score":0.42809998989105225},{"id":"https://openalex.org/keywords/channel","display_name":"Channel (broadcasting)","score":0.4146000146865845}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6841999888420105},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.678600013256073},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6251999735832214},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.6061999797821045},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.5422000288963318},{"id":"https://openalex.org/C2777361361","wikidata":"https://www.wikidata.org/wiki/Q1112585","display_name":"Polarity (international relations)","level":3,"score":0.5414000153541565},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.4584999978542328},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.4374000132083893},{"id":"https://openalex.org/C206310091","wikidata":"https://www.wikidata.org/wiki/Q750859","display_name":"Emotion classification","level":2,"score":0.42809998989105225},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.4146000146865845},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4004000127315521},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.3783999979496002},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.3546000123023987},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3215000033378601},{"id":"https://openalex.org/C185874996","wikidata":"https://www.wikidata.org/wiki/Q269699","display_name":"Interdependence","level":2,"score":0.31470000743865967},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.30079999566078186},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.28760001063346863},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2872999906539917},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.2842000126838684},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.26089999079704285}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3343031.3351062","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3343031.3351062","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 27th ACM International Conference on Multimedia","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:1909.05693","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1909.05693","pdf_url":"https://arxiv.org/pdf/1909.05693","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"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1909.05693","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1909.05693","pdf_url":"https://arxiv.org/pdf/1909.05693","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":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":63,"referenced_works":["https://openalex.org/W1861492603","https://openalex.org/W1950412479","https://openalex.org/W1984942926","https://openalex.org/W2003856922","https://openalex.org/W2016713906","https://openalex.org/W2046682605","https://openalex.org/W2047170290","https://openalex.org/W2051308385","https://openalex.org/W2056553798","https://openalex.org/W2062530052","https://openalex.org/W2063948594","https://openalex.org/W2074356411","https://openalex.org/W2075456404","https://openalex.org/W2075953807","https://openalex.org/W2078833921","https://openalex.org/W2078861472","https://openalex.org/W2085940040","https://openalex.org/W2088511157","https://openalex.org/W2105931454","https://openalex.org/W2108598243","https://openalex.org/W2118526556","https://openalex.org/W2153049579","https://openalex.org/W2153635508","https://openalex.org/W2194775991","https://openalex.org/W2220981600","https://openalex.org/W2293332611","https://openalex.org/W2302086703","https://openalex.org/W2430424407","https://openalex.org/W2460852148","https://openalex.org/W2497880326","https://openalex.org/W2507296351","https://openalex.org/W2515036155","https://openalex.org/W2525668096","https://openalex.org/W2531468424","https://openalex.org/W2547146855","https://openalex.org/W2550553598","https://openalex.org/W2552972371","https://openalex.org/W2575842049","https://openalex.org/W2621571501","https://openalex.org/W2735673432","https://openalex.org/W2739107216","https://openalex.org/W2739474071","https://openalex.org/W2740046088","https://openalex.org/W2741561025","https://openalex.org/W2741630455","https://openalex.org/W2745461083","https://openalex.org/W2745497104","https://openalex.org/W2752782242","https://openalex.org/W2765354427","https://openalex.org/W2766251611","https://openalex.org/W2793857798","https://openalex.org/W2798503473","https://openalex.org/W2805121932","https://openalex.org/W2883853499","https://openalex.org/W2884585870","https://openalex.org/W2896591327","https://openalex.org/W2908347420","https://openalex.org/W2913218058","https://openalex.org/W2962858109","https://openalex.org/W2963656855","https://openalex.org/W2963954913","https://openalex.org/W2981843773","https://openalex.org/W6600648412"],"related_works":[],"abstract_inverted_index":{"Existing":[0],"methods":[1,24],"on":[2,8,47,123,158],"visual":[3,44,90,183],"emotion":[4,10,20,73,91,127,151,184],"analysis":[5],"mainly":[6],"focus":[7],"coarse-grained":[9],"classification,":[11],"i.e.":[12,117],"assigning":[13],"an":[14,72],"image":[15],"with":[16,71],"a":[17,55,61,69,87,113,142,178],"dominant":[18],"discrete":[19],"category.":[21],"However,":[22],"these":[23],"cannot":[25],"well":[26],"reflect":[27],"the":[28,39,96,105,124,131,136,150,159,166,170,174],"complexity":[29],"and":[30,83,104,147,162,165],"subtlety":[31],"of":[32,43],"emotions.":[33],"In":[34],"this":[35],"paper,":[36],"we":[37,53,77,111],"study":[38],"fine-grained":[40,182],"regression":[41,115,119,152],"problem":[42],"emotions":[45],"based":[46,122],"convolutional":[48],"neural":[49],"networks":[50],"(CNNs).":[51],"Specifically,":[52],"develop":[54],"Polarity-consistent":[56],"Deep":[57],"Attention":[58],"Network":[59],"(PDANet),":[60],"novel":[62,114],"network":[63],"architecture":[64],"that":[65,169],"integrates":[66],"attention":[67,132,145],"into":[68,86],"CNN":[70,88],"polarity":[74,128,143],"constraint.":[75],"First,":[76],"propose":[78],"to":[79,129],"incorporate":[80],"both":[81],"spatial":[82,98],"channel-wise":[84],"attentions":[85],"for":[89,181],"regression,":[92],"which":[93],"jointly":[94],"considers":[95],"local":[97],"connectivity":[99],"patterns":[100],"along":[101],"each":[102],"channel":[103],"interdependency":[106],"between":[107],"different":[108],"channels.":[109],"Second,":[110],"design":[112],"loss,":[116,121,138],"polarity-consistent":[118],"(PCR)":[120],"weakly":[125],"supervised":[126],"guide":[130],"generation.":[133],"By":[134],"optimizing":[135],"PCR":[137],"PDANet":[139,172],"can":[140],"generate":[141],"preserved":[144],"map":[146],"thus":[148],"improve":[149],"performance.":[153],"Extensive":[154],"experiments":[155],"are":[156],"conducted":[157],"IAPS,":[160],"NAPS,":[161],"EMOTIC":[163],"datasets,":[164],"results":[167],"demonstrate":[168],"proposed":[171],"outperforms":[173],"state-of-the-art":[175],"approaches":[176],"by":[177],"large":[179],"margin":[180],"regression.":[185],"Our":[186],"source":[187],"code":[188],"is":[189],"released":[190],"at:":[191],"https://github.com/ZizhouJia/PDANet.":[192]},"counts_by_year":[{"year":2025,"cited_by_count":12},{"year":2024,"cited_by_count":16},{"year":2023,"cited_by_count":17},{"year":2022,"cited_by_count":9},{"year":2021,"cited_by_count":10},{"year":2020,"cited_by_count":12},{"year":2019,"cited_by_count":1}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2019-09-19T00:00:00"}
