{"id":"https://openalex.org/W4210347936","doi":"https://doi.org/10.1109/tmm.2022.3144804","title":"Multiscale Emotion Representation Learning for Affective Image Recognition","display_name":"Multiscale Emotion Representation Learning for Affective Image Recognition","publication_year":2022,"publication_date":"2022-01-25","ids":{"openalex":"https://openalex.org/W4210347936","doi":"https://doi.org/10.1109/tmm.2022.3144804"},"language":"en","primary_location":{"id":"doi:10.1109/tmm.2022.3144804","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tmm.2022.3144804","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":["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/A5000801361","display_name":"Haimin Zhang","orcid":"https://orcid.org/0000-0002-0021-3634"},"institutions":[{"id":"https://openalex.org/I114017466","display_name":"University of Technology Sydney","ror":"https://ror.org/03f0f6041","country_code":"AU","type":"education","lineage":["https://openalex.org/I114017466"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Haimin Zhang","raw_affiliation_strings":["School of Electrical and Data Engineering and Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW, Australia"],"raw_orcid":"https://orcid.org/0000-0002-0021-3634","affiliations":[{"raw_affiliation_string":"School of Electrical and Data Engineering and Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW, Australia","institution_ids":["https://openalex.org/I114017466"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100413849","display_name":"Min Xu","orcid":"https://orcid.org/0000-0001-9581-8849"},"institutions":[{"id":"https://openalex.org/I114017466","display_name":"University of Technology Sydney","ror":"https://ror.org/03f0f6041","country_code":"AU","type":"education","lineage":["https://openalex.org/I114017466"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Min Xu","raw_affiliation_strings":["School of Electrical and Data Engineering and Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW, Australia"],"raw_orcid":"https://orcid.org/0000-0001-9581-8849","affiliations":[{"raw_affiliation_string":"School of Electrical and Data Engineering and Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW, Australia","institution_ids":["https://openalex.org/I114017466"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I114017466"],"apc_list":null,"apc_paid":null,"fwci":2.3238,"has_fulltext":false,"cited_by_count":23,"citation_normalized_percentile":{"value":0.89427326,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":"25","issue":null,"first_page":"2203","last_page":"2212"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.9994000196456909,"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":0.9994000196456909,"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":0.9965999722480774,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9950000047683716,"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/computer-science","display_name":"Computer science","score":0.8411002159118652},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6839330196380615},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6221315860748291},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5639521479606628},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5312243103981018},{"id":"https://openalex.org/keywords/emotion-recognition","display_name":"Emotion recognition","score":0.5217938423156738},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.521178662776947},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.47934943437576294},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4656178951263428},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.45087337493896484},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.4485973119735718},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4456581771373749},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4335657060146332},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.38992851972579956}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8411002159118652},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6839330196380615},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6221315860748291},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5639521479606628},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5312243103981018},{"id":"https://openalex.org/C2777438025","wikidata":"https://www.wikidata.org/wiki/Q1339090","display_name":"Emotion recognition","level":2,"score":0.5217938423156738},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.521178662776947},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.47934943437576294},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4656178951263428},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.45087337493896484},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.4485973119735718},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4456581771373749},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4335657060146332},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.38992851972579956},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","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/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tmm.2022.3144804","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tmm.2022.3144804","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"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":68,"referenced_works":["https://openalex.org/W7746136","https://openalex.org/W44741455","https://openalex.org/W189596042","https://openalex.org/W603908379","https://openalex.org/W639708223","https://openalex.org/W1506209491","https://openalex.org/W1536680647","https://openalex.org/W1686810756","https://openalex.org/W1784731433","https://openalex.org/W1836465849","https://openalex.org/W1930223417","https://openalex.org/W1981424477","https://openalex.org/W2003856922","https://openalex.org/W2047170290","https://openalex.org/W2074356411","https://openalex.org/W2095705004","https://openalex.org/W2102539767","https://openalex.org/W2117539524","https://openalex.org/W2118526556","https://openalex.org/W2151103935","https://openalex.org/W2153959628","https://openalex.org/W2194775991","https://openalex.org/W2295107390","https://openalex.org/W2347880541","https://openalex.org/W2475223347","https://openalex.org/W2517991028","https://openalex.org/W2552066052","https://openalex.org/W2585123518","https://openalex.org/W2618530766","https://openalex.org/W2740046088","https://openalex.org/W2741630455","https://openalex.org/W2766094568","https://openalex.org/W2768242641","https://openalex.org/W2793857798","https://openalex.org/W2794257965","https://openalex.org/W2887175137","https://openalex.org/W2902923584","https://openalex.org/W2907492528","https://openalex.org/W2908347420","https://openalex.org/W2928165649","https://openalex.org/W2954137266","https://openalex.org/W2963150697","https://openalex.org/W2963446712","https://openalex.org/W2963840672","https://openalex.org/W2963992782","https://openalex.org/W2971765953","https://openalex.org/W3001529617","https://openalex.org/W3035160371","https://openalex.org/W3042085267","https://openalex.org/W3046620880","https://openalex.org/W3104752576","https://openalex.org/W3106925514","https://openalex.org/W3185204125","https://openalex.org/W4285723986","https://openalex.org/W4295312788","https://openalex.org/W4385245566","https://openalex.org/W6601925561","https://openalex.org/W6607775107","https://openalex.org/W6618372016","https://openalex.org/W6637373629","https://openalex.org/W6638212731","https://openalex.org/W6638667902","https://openalex.org/W6674330103","https://openalex.org/W6675549610","https://openalex.org/W6696085341","https://openalex.org/W6739901393","https://openalex.org/W6746451923","https://openalex.org/W6766978945"],"related_works":["https://openalex.org/W2378211422","https://openalex.org/W2745001401","https://openalex.org/W4321353415","https://openalex.org/W2130974462","https://openalex.org/W972276598","https://openalex.org/W4246352526","https://openalex.org/W2028665553","https://openalex.org/W4230315250","https://openalex.org/W4309346246","https://openalex.org/W3204853141"],"abstract_inverted_index":{"Recognition":[0],"of":[1,36,82],"emotions":[2],"conveyed":[3],"in":[4,154],"images":[5],"has":[6],"attracted":[7],"increasing":[8],"research":[9],"attention.":[10],"Recent":[11],"studies":[12,27],"show":[13,144],"that":[14,60,145,182],"leveraging":[15],"local":[16,38,65],"affective":[17,39,66,75,84,104,119],"regions":[18,105],"helps":[19],"to":[20,101,112,193],"improve":[21],"the":[22,33,37,64,71,114,118,123,149,156,162,183],"recognition":[23,157,169],"performance.":[24,158],"However,":[25],"these":[26],"do":[28],"not":[29],"consider":[30],"features":[31,62,127,147],"from":[32,106,148],"broad":[34,72,150],"context":[35,73,151],"regions,":[40],"which":[41],"could":[42],"provide":[43],"useful":[44],"information":[45],"for":[46,63,74,139,165],"learning":[47,92],"improved":[48,187],"emotion":[49,141,168],"representations.":[50],"In":[51],"this":[52],"paper,":[53],"we":[54],"present":[55],"a":[56,89,107,134],"region-based":[57],"multiscale":[58,90],"network":[59,80,111,138,164,185],"learns":[61],"region":[67,85,120],"as":[68,70,191],"well":[69],"image":[76],"recognition.":[77],"The":[78,94,178],"proposed":[79,163,184],"consists":[81],"an":[83],"detection":[86,115,124],"module":[87],"and":[88,130,170],"feature":[91],"module.":[93,116],"class":[95],"activation":[96],"mapping":[97],"method":[98],"is":[99,152],"used":[100],"generate":[102],"pseudo":[103],"pretrained":[108],"deep":[109],"neural":[110],"train":[113],"For":[117],"outputted":[121],"by":[122,133],"module,":[125],"three-scale":[126],"are":[128],"extracted":[129],"then":[131],"encoded":[132],"kernel-based":[135],"graph":[136],"attention":[137],"final":[140],"classification.":[142],"We":[143,159],"integrating":[146],"effective":[153],"improving":[155],"experimentally":[160],"evaluate":[161],"both":[166],"multi-class":[167],"binary":[171],"sentiment":[172],"classification":[173],"on":[174],"different":[175],"benchmark":[176],"datasets.":[177],"experimental":[179],"results":[180],"demonstrate":[181],"achieves":[186],"or":[188],"comparable":[189],"performance":[190],"compared":[192],"previous":[194],"state-of-the-art":[195],"models.":[196]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":7},{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":4}],"updated_date":"2026-08-04T08:18:43.703281","created_date":"2025-10-10T00:00:00"}
