{"id":"https://openalex.org/W4387968611","doi":"https://doi.org/10.1145/3581783.3612186","title":"Progressive Visual Content Understanding Network for Image Emotion Classification","display_name":"Progressive Visual Content Understanding Network for Image Emotion Classification","publication_year":2023,"publication_date":"2023-10-26","ids":{"openalex":"https://openalex.org/W4387968611","doi":"https://doi.org/10.1145/3581783.3612186"},"language":"en","primary_location":{"id":"doi:10.1145/3581783.3612186","doi":"https://doi.org/10.1145/3581783.3612186","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3581783.3612186","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3581783.3612186","source":{"id":"https://openalex.org/S4363608757","display_name":"ACM International Conference on Multimedia (ACM MM)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM International Conference on Multimedia","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","is_traditional":true,"indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"bronze","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3581783.3612186","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5081132731","display_name":"Jicai Pan","orcid":"https://orcid.org/0000-0002-7935-3452","observed_orcids":["https://orcid.org/0000-0002-7935-3452"]},"institutions":[{"id":"https://openalex.org/I126520041","display_name":"University of Science and Technology of China","ror":"https://ror.org/04c4dkn09","country_code":"CN","type":"education","lineage":["https://openalex.org/I126520041","https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jicai Pan","raw_affiliation_strings":["University of Science and Technology of China, Hefei, China"],"raw_orcid":"https://orcid.org/0000-0002-7935-3452","affiliations":[{"raw_affiliation_string":"University of Science and Technology of China, Hefei, China","institution_ids":["https://openalex.org/I126520041"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5077046519","display_name":"Shangfei Wang","orcid":"https://orcid.org/0000-0003-1164-9895","observed_orcids":["https://orcid.org/0000-0003-1164-9895"]},"institutions":[{"id":"https://openalex.org/I126520041","display_name":"University of Science and Technology of China","ror":"https://ror.org/04c4dkn09","country_code":"CN","type":"education","lineage":["https://openalex.org/I126520041","https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shangfei Wang","raw_affiliation_strings":["University of Science and Technology of China &amp; Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China"],"raw_orcid":"https://orcid.org/0000-0003-1164-9895","affiliations":[{"raw_affiliation_string":"University of Science and Technology of China &amp; Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China","institution_ids":["https://openalex.org/I126520041"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I126520041"],"apc_list":null,"apc_paid":null,"fwci":0.68,"has_fulltext":true,"cited_by_count":6,"citation_normalized_percentile":{"value":0.74517375,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":93,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"6034","last_page":"6044"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10667","display_name":"Emotion and Mood Recognition","score":0.5536999702453613,"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/T10667","display_name":"Emotion and Mood Recognition","score":0.5536999702453613,"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/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.22419999539852142,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.11150000244379044,"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/pseudo-labeling","display_name":"pseudo-labeling","score":0.6460000276565552},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"image segmentation","score":0.902999997138977},{"id":"https://openalex.org/keywords/vision-language-models","display_name":"vision-language models","score":0.9929999709129333},{"id":"https://openalex.org/keywords/hierarchical-feature-extraction","display_name":"hierarchical feature extraction","score":0.7630000114440918},{"id":"https://openalex.org/keywords/progressive-learning","display_name":"progressive learning","score":0.5490000247955322}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7863795757293701},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.621329128742218},{"id":"https://openalex.org/C26760741","wikidata":"https://www.wikidata.org/wiki/Q160402","display_name":"Perception","level":2,"score":0.6118792295455933},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6068939566612244},{"id":"https://openalex.org/C206310091","wikidata":"https://www.wikidata.org/wiki/Q750859","display_name":"Emotion classification","level":2,"score":0.5193301439285278},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5042818784713745},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.4576945900917053},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.42463240027427673},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.42421141266822815},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4126133620738983},{"id":"https://openalex.org/C86034646","wikidata":"https://www.wikidata.org/wiki/Q474311","display_name":"Semantic gap","level":4,"score":0.4113510251045227},{"id":"https://openalex.org/C2776141551","wikidata":"https://www.wikidata.org/wiki/Q16000087","display_name":"Emotion perception","level":3,"score":0.41079920530319214},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3671213984489441},{"id":"https://openalex.org/C1667742","wikidata":"https://www.wikidata.org/wiki/Q10927554","display_name":"Image retrieval","level":3,"score":0.14444512128829956},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C195704467","wikidata":"https://www.wikidata.org/wiki/Q327968","display_name":"Facial expression","level":2,"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/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C169760540","wikidata":"https://www.wikidata.org/wiki/Q207011","display_name":"Neuroscience","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":1,"locations":[{"id":"doi:10.1145/3581783.3612186","doi":"https://doi.org/10.1145/3581783.3612186","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3581783.3612186","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3581783.3612186","source":{"id":"https://openalex.org/S4363608757","display_name":"ACM International Conference on Multimedia (ACM MM)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM International Conference on Multimedia","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3581783.3612186","doi":"https://doi.org/10.1145/3581783.3612186","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3581783.3612186","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3581783.3612186","source":{"id":"https://openalex.org/S4363608757","display_name":"ACM International Conference on Multimedia (ACM MM)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM International Conference on Multimedia","raw_type":"proceedings-article"},"sustainable_development_goals":[],"study_designs":[],"awards":[{"id":"https://openalex.org/G8472469554","display_name":null,"funder_award_id":"92048203","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4387968611.pdf","grobid_xml":"https://content.openalex.org/works/W4387968611.grobid-xml"},"referenced_works_count":35,"referenced_works":["https://openalex.org/W1784731433","https://openalex.org/W1875160599","https://openalex.org/W1950412479","https://openalex.org/W1995681409","https://openalex.org/W2003856922","https://openalex.org/W2048783874","https://openalex.org/W2055387001","https://openalex.org/W2070148066","https://openalex.org/W2074356411","https://openalex.org/W2075456404","https://openalex.org/W2117539524","https://openalex.org/W2125387256","https://openalex.org/W2134927309","https://openalex.org/W2163605009","https://openalex.org/W2194775991","https://openalex.org/W2347880541","https://openalex.org/W2798503473","https://openalex.org/W2887175137","https://openalex.org/W2923002906","https://openalex.org/W2963992782","https://openalex.org/W2971671927","https://openalex.org/W2972850892","https://openalex.org/W3030520226","https://openalex.org/W3042085267","https://openalex.org/W3094502228","https://openalex.org/W3128968443","https://openalex.org/W3174134652","https://openalex.org/W3180659539","https://openalex.org/W3206577156","https://openalex.org/W3211094633","https://openalex.org/W4312986439","https://openalex.org/W4313042449","https://openalex.org/W4386071839","https://openalex.org/W4390873343","https://openalex.org/W6602008033"],"related_works":["https://openalex.org/W4392607674","https://openalex.org/W4224323398","https://openalex.org/W3090855697","https://openalex.org/W4411849630","https://openalex.org/W4389492440","https://openalex.org/W4416951117","https://openalex.org/W4313042449","https://openalex.org/W4392761670","https://openalex.org/W4408280866","https://openalex.org/W4400912673"],"abstract_inverted_index":{"Most":[0],"existing":[1],"methods":[2],"for":[3,138,155,173],"image":[4,129,174],"emotion":[5,67,107,111,156,175],"classification":[6],"extract":[7,81,152],"features":[8,35,112,154],"directly":[9],"from":[10,91],"images":[11],"supervised":[12],"by":[13,46,63],"a":[14,22,57],"single":[15],"emotional":[16],"label.":[17],"However,":[18],"this":[19,54],"approach":[20],"has":[21],"limitation":[23],"known":[24],"as":[25,36],"the":[26,31,43,51,64,99,116,148],"affective":[27,52],"gap":[28],"which":[29],"restricts":[30],"capability":[32],"of":[33,75,102,124],"these":[34],"they":[37],"do":[38],"not":[39],"always":[40],"align":[41],"with":[42],"emotions":[44],"perceived":[45],"users.":[47],"To":[48],"effectively":[49],"bridge":[50],"gap,":[53],"paper":[55],"proposes":[56],"visual":[58],"content":[59,101],"understanding":[60,146],"network":[61,72,139,149,165],"inspired":[62],"human":[65],"staged":[66],"perception":[68,77,87,96,108],"process.":[69],"The":[70,141],"proposed":[71],"is":[73],"comprised":[74],"three":[76],"modules":[78],"designed":[79],"to":[80,134,150],"multi-level":[82],"information.":[83,120],"Firstly,":[84],"an":[85,94,106],"entity":[86,117,143],"module":[88,97,109],"extracts":[89,98,110],"entities":[90,125],"images.":[92],"Secondly,":[93],"attribute":[95,100,119,145],"each":[103],"entity.":[104],"Thirdly,":[105],"based":[113],"on":[114,169],"both":[115],"and":[118,126,131,144],"We":[121],"generate":[122],"pseudo-labels":[123],"attributes":[127],"through":[128],"segmentation":[130],"vision-language":[132],"models":[133],"provide":[135],"auxiliary":[136],"guidance":[137],"learning.":[140],"progressive":[142,163],"enable":[147],"hierarchically":[151],"semantic-level":[153],"analysis.":[157],"Extensive":[158],"experiments":[159],"demonstrate":[160],"that":[161],"our":[162],"learning":[164],"achieves":[166],"superior":[167],"performance":[168],"various":[170],"benchmark":[171],"datasets":[172],"classification.":[176]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":2}],"updated_date":"2026-11-07T11:25:07.637180","created_date":"2023-10-28T00:00:00"}
