{"id":"https://openalex.org/W2798503473","doi":"https://doi.org/10.1109/cvpr.2018.00791","title":"Weakly Supervised Coupled Networks for Visual Sentiment Analysis","display_name":"Weakly Supervised Coupled Networks for Visual Sentiment Analysis","publication_year":2018,"publication_date":"2018-06-01","ids":{"openalex":"https://openalex.org/W2798503473","doi":"https://doi.org/10.1109/cvpr.2018.00791","mag":"2798503473"},"language":"en","primary_location":{"id":"doi:10.1109/cvpr.2018.00791","doi":"https://doi.org/10.1109/cvpr.2018.00791","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr.2018.00791","pdf_url":null,"source":{"id":"https://openalex.org/S4210176548","display_name":"IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings","issn_l":"1063-6919","issn":["1063-6919","2332-564X","2575-7075"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","norway-1"],"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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition","raw_type":"proceedings-article"},"volume":null,"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/A5089409678","display_name":"Jufeng Yang","orcid":"https://orcid.org/0000-0003-0219-3443","observed_orcids":["https://orcid.org/0000-0003-0219-3443"]},"institutions":[{"id":"https://openalex.org/I205237279","display_name":"Nankai University","ror":"https://ror.org/01y1kjr75","country_code":"CN","type":"education","lineage":["https://openalex.org/I205237279"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jufeng Yang","raw_affiliation_strings":["College of Computer and Control Engineering, Nankai University, Tianjin, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Computer and Control Engineering, Nankai University, Tianjin, China","institution_ids":["https://openalex.org/I205237279"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5005084059","display_name":"Dongyu She","orcid":"https://orcid.org/0000-0002-1434-562X","observed_orcids":["https://orcid.org/0000-0002-1434-562X"]},"institutions":[{"id":"https://openalex.org/I205237279","display_name":"Nankai University","ror":"https://ror.org/01y1kjr75","country_code":"CN","type":"education","lineage":["https://openalex.org/I205237279"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dongyu She","raw_affiliation_strings":["College of Computer and Control Engineering, Nankai University, Tianjin, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Computer and Control Engineering, Nankai University, Tianjin, China","institution_ids":["https://openalex.org/I205237279"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5067850699","display_name":"Yu\u2010Kun Lai","orcid":"https://orcid.org/0000-0002-2094-5680","observed_orcids":["https://orcid.org/0000-0002-2094-5680"]},"institutions":[{"id":"https://openalex.org/I79510175","display_name":"Cardiff University","ror":"https://ror.org/03kk7td41","country_code":"GB","type":"education","lineage":["https://openalex.org/I79510175"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Yu-Kun Lai","raw_affiliation_strings":["School of Computer Science and Informatics, Cardiff University, Cardiff, UK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Informatics, Cardiff University, Cardiff, UK","institution_ids":["https://openalex.org/I79510175"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068129380","display_name":"Paul L. Rosin","orcid":"https://orcid.org/0000-0002-4965-3884","observed_orcids":["https://orcid.org/0000-0002-4965-3884"]},"institutions":[{"id":"https://openalex.org/I79510175","display_name":"Cardiff University","ror":"https://ror.org/03kk7td41","country_code":"GB","type":"education","lineage":["https://openalex.org/I79510175"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Paul L. Rosin","raw_affiliation_strings":["School of Computer Science and Informatics, Cardiff University, Cardiff, UK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Informatics, Cardiff University, Cardiff, UK","institution_ids":["https://openalex.org/I79510175"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100418319","display_name":"Ming\u2013Hsuan Yang","orcid":"https://orcid.org/0000-0003-4848-2304","observed_orcids":["https://orcid.org/0000-0003-4848-2304","https://orcid.org/0000-0001-6364-0417"]},"institutions":[{"id":"https://openalex.org/I156087764","display_name":"University of California, Merced","ror":"https://ror.org/00d9ah105","country_code":"US","type":"education","lineage":["https://openalex.org/I156087764"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ming-Hsuan Yang","raw_affiliation_strings":["School of Engineering, University of California, Merced, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Engineering, University of California, Merced, USA","institution_ids":["https://openalex.org/I156087764"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":7.3883,"has_fulltext":false,"cited_by_count":158,"citation_normalized_percentile":{"value":0.97793704,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"7584","last_page":"7592"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.5288000106811523,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.5288000106811523,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T10667","display_name":"Emotion and Mood Recognition","score":0.42719998955726624,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.021900000050663948,"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/visual-sentiment-analysis","display_name":"visual sentiment analysis","score":0.9990000128746033},{"id":"https://openalex.org/keywords/weakly-supervised-learning","display_name":"weakly supervised learning","score":0.9959999918937683},{"id":"https://openalex.org/keywords/image-level-labels","display_name":"image-level labels","score":0.7260000109672546},{"id":"https://openalex.org/keywords/end-to-end-optimization","display_name":"end-to-end optimization","score":0.6269999742507935}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8369349241256714},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7228295803070068},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7146655321121216},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.7117469906806946},{"id":"https://openalex.org/C66402592","wikidata":"https://www.wikidata.org/wiki/Q2271421","display_name":"Sentiment analysis","level":2,"score":0.6852260828018188},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.6821804046630859},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5640466213226318},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.5328691005706787},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4931163489818573},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.49014410376548767},{"id":"https://openalex.org/C124304363","wikidata":"https://www.wikidata.org/wiki/Q673661","display_name":"Abstraction","level":2,"score":0.4602246880531311},{"id":"https://openalex.org/C2776321320","wikidata":"https://www.wikidata.org/wiki/Q857525","display_name":"Annotation","level":2,"score":0.45056384801864624},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.45039504766464233},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/cvpr.2018.00791","doi":"https://doi.org/10.1109/cvpr.2018.00791","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr.2018.00791","pdf_url":null,"source":{"id":"https://openalex.org/S4210176548","display_name":"IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings","issn_l":"1063-6919","issn":["1063-6919","2332-564X","2575-7075"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","norway-1"],"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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition","raw_type":"proceedings-article"},{"id":"pmh:oai:https://orca.cardiff.ac.uk:110340","doi":null,"is_oa":false,"landing_page_url":"https://orca.cardiff.ac.uk/id/eprint/110340/1/Weakly%202108.pdf","pdf_url":"https://orca.cardiff.ac.uk/id/eprint/110340/1/Weakly%202108.pdf","source":{"id":"https://openalex.org/S4306401195","display_name":"ORCA Online Research @Cardiff (Cardiff University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I79510175","host_organization_name":"Cardiff University","host_organization_lineage":["https://openalex.org/I79510175"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":null,"raw_type":"PeerReviewed"}],"best_oa_location":null,"sustainable_development_goals":[],"sustainable_development_goals_aurora":[{"score":0.5,"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities"}],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":42,"referenced_works":["https://openalex.org/W1536680647","https://openalex.org/W1686810756","https://openalex.org/W1784731433","https://openalex.org/W1849277567","https://openalex.org/W1950412479","https://openalex.org/W1994488211","https://openalex.org/W2003856922","https://openalex.org/W2056553798","https://openalex.org/W2063948594","https://openalex.org/W2066624635","https://openalex.org/W2074356411","https://openalex.org/W2075456404","https://openalex.org/W2108598243","https://openalex.org/W2133324800","https://openalex.org/W2153049579","https://openalex.org/W2153635508","https://openalex.org/W2163605009","https://openalex.org/W2194775991","https://openalex.org/W2295107390","https://openalex.org/W2338548859","https://openalex.org/W2347880541","https://openalex.org/W2403015048","https://openalex.org/W2513550067","https://openalex.org/W2517991028","https://openalex.org/W2525668096","https://openalex.org/W2552972371","https://openalex.org/W2604737966","https://openalex.org/W2604773025","https://openalex.org/W2738853914","https://openalex.org/W2741561025","https://openalex.org/W2741630455","https://openalex.org/W2770912167","https://openalex.org/W2793857798","https://openalex.org/W2962835968","https://openalex.org/W2963603913","https://openalex.org/W2963795442","https://openalex.org/W2963992782","https://openalex.org/W4298372712","https://openalex.org/W6637373629","https://openalex.org/W6639204139","https://openalex.org/W6662642538","https://openalex.org/W6684191040"],"related_works":["https://openalex.org/W2953234277","https://openalex.org/W2626256601","https://openalex.org/W2900413183","https://openalex.org/W4390975304","https://openalex.org/W147410782","https://openalex.org/W3022252430","https://openalex.org/W4287804464","https://openalex.org/W3103989898","https://openalex.org/W3211292372","https://openalex.org/W803346624"],"abstract_inverted_index":{"Automatic":[0],"assessment":[1],"of":[2,15,26],"sentiment":[3,28,46,88,128,139,176],"from":[4,48],"visual":[5,27,175],"content":[6],"has":[7],"gained":[8],"considerable":[9],"attention":[10],"with":[11,75,98,130],"the":[12,24,31,35,49,63,80,99,112,120,127,138,151,165,171],"increasing":[13],"tendency":[14],"expressing":[16],"opinions":[17],"on-line.":[18],"In":[19],"this":[20],"paper,":[21],"we":[22],"solve":[23],"problem":[25],"analysis":[29],"using":[30],"high-level":[32],"abstraction":[33],"in":[34,153],"recognition":[36],"process.":[37],"Existing":[38],"methods":[39,173],"based":[40],"on":[41,62,159],"convolutional":[42,73,96],"neural":[43],"networks":[44],"learn":[45],"representations":[47],"holistic":[50,121],"image":[51,55],"appearance.":[52],"However,":[53],"different":[54,60],"regions":[56],"can":[57],"have":[58],"a":[59,69,87,94,145],"influence":[61],"intended":[64],"expression.":[65],"This":[66],"paper":[67],"presents":[68],"weakly":[70],"supervised":[71],"coupled":[72],"network":[74,97,152],"two":[76],"branches":[77,143],"to":[78],"leverage":[79],"localized":[81,123],"information.":[82],"The":[83,115],"first":[84],"branch":[85,117],"detects":[86],"specific":[89],"soft":[90],"map":[91,129],"by":[92,125],"training":[93],"fully":[95],"cross":[100],"spatial":[101],"pooling":[102],"strategy,":[103],"which":[104],"only":[105],"requires":[106],"image-level":[107],"labels,":[108],"thereby":[109],"significantly":[110],"reducing":[111],"annotation":[113],"burden.":[114],"second":[116],"utilizes":[118],"both":[119],"and":[122,141,149],"information":[124],"coupling":[126],"deep":[131,147],"features":[132],"for":[133,174],"robust":[134],"classification.":[135],"We":[136],"integrate":[137],"detection":[140],"classification":[142],"into":[144],"unified":[146],"framework":[148],"optimize":[150],"an":[154],"end-to-end":[155],"manner.":[156],"Extensive":[157],"experiments":[158],"six":[160],"benchmark":[161],"datasets":[162],"demonstrate":[163],"that":[164],"proposed":[166],"method":[167],"performs":[168],"favorably":[169],"against":[170],"state-of-the-art":[172],"analysis.":[177]},"counts_by_year":[{"year":2026,"cited_by_count":4},{"year":2025,"cited_by_count":17},{"year":2024,"cited_by_count":20},{"year":2023,"cited_by_count":22},{"year":2022,"cited_by_count":24},{"year":2021,"cited_by_count":29},{"year":2020,"cited_by_count":20},{"year":2019,"cited_by_count":20},{"year":2018,"cited_by_count":2}],"updated_date":"2026-10-14T16:39:01.820549","created_date":"2025-10-10T00:00:00"}