{"id":"https://openalex.org/W2801154741","doi":"https://doi.org/10.1109/icassp.2018.8462686","title":"A Deep Reinforcement Learning Framework for Identifying Funny Scenes in Movies","display_name":"A Deep Reinforcement Learning Framework for Identifying Funny Scenes in Movies","publication_year":2018,"publication_date":"2018-04-01","ids":{"openalex":"https://openalex.org/W2801154741","doi":"https://doi.org/10.1109/icassp.2018.8462686","mag":"2801154741"},"language":"en","primary_location":{"id":"doi:10.1109/icassp.2018.8462686","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2018.8462686","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-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/A5101654853","display_name":"Haoqi Li","orcid":"https://orcid.org/0000-0002-8851-4529"},"institutions":[{"id":"https://openalex.org/I1174212","display_name":"University of Southern California","ror":"https://ror.org/03taz7m60","country_code":"US","type":"education","lineage":["https://openalex.org/I1174212"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Haoqi Li","raw_affiliation_strings":["University of Southern California, Los Angeles, CA, USA"],"affiliations":[{"raw_affiliation_string":"University of Southern California, Los Angeles, CA, USA","institution_ids":["https://openalex.org/I1174212"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103571732","display_name":"Naveen Kumar","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Naveen Kumar","raw_affiliation_strings":["SONY Interactive Entertainment LLC, San Mateo, CA, USA"],"affiliations":[{"raw_affiliation_string":"SONY Interactive Entertainment LLC, San Mateo, CA, USA","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5081135618","display_name":"Ruxin Chen","orcid":"https://orcid.org/0009-0000-0624-4127"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ruxin Chen","raw_affiliation_strings":["SONY Interactive Entertainment LLC, San Mateo, CA, USA"],"affiliations":[{"raw_affiliation_string":"SONY Interactive Entertainment LLC, San Mateo, CA, USA","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5021678540","display_name":"Panayiotis Georgiou","orcid":"https://orcid.org/0000-0002-0790-7161"},"institutions":[{"id":"https://openalex.org/I1174212","display_name":"University of Southern California","ror":"https://ror.org/03taz7m60","country_code":"US","type":"education","lineage":["https://openalex.org/I1174212"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Panayiotis Georgiou","raw_affiliation_strings":["University of Southern California, Los Angeles, CA, USA"],"affiliations":[{"raw_affiliation_string":"University of Southern California, Los Angeles, CA, USA","institution_ids":["https://openalex.org/I1174212"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5101654853"],"corresponding_institution_ids":["https://openalex.org/I1174212"],"apc_list":null,"apc_paid":null,"fwci":1.972,"has_fulltext":false,"cited_by_count":19,"citation_normalized_percentile":{"value":0.86301862,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":"550","issue":null,"first_page":"3116","last_page":"3120"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10667","display_name":"Emotion and Mood Recognition","score":0.9879000186920166,"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"}},"topics":[{"id":"https://openalex.org/T10667","display_name":"Emotion and Mood Recognition","score":0.9879000186920166,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.9854000210762024,"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/T10462","display_name":"Reinforcement Learning in Robotics","score":0.9696999788284302,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8399429321289062},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.7210752367973328},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7152047157287598},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.559619128704071},{"id":"https://openalex.org/keywords/ground-truth","display_name":"Ground truth","score":0.5309476256370544},{"id":"https://openalex.org/keywords/face","display_name":"Face (sociological concept)","score":0.5054786205291748},{"id":"https://openalex.org/keywords/perception","display_name":"Perception","score":0.5004456043243408},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.41779056191444397},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3928806185722351}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8399429321289062},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.7210752367973328},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7152047157287598},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.559619128704071},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.5309476256370544},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.5054786205291748},{"id":"https://openalex.org/C26760741","wikidata":"https://www.wikidata.org/wiki/Q160402","display_name":"Perception","level":2,"score":0.5004456043243408},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.41779056191444397},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3928806185722351},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"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/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C36289849","wikidata":"https://www.wikidata.org/wiki/Q34749","display_name":"Social science","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}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/icassp.2018.8462686","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2018.8462686","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"},{"id":"mag:3176809412","is_oa":false,"landing_page_url":"https://jglobal.jst.go.jp/en/detail?JGLOBAL_ID=201802286215586458","pdf_url":null,"source":{"id":"https://openalex.org/S4306512817","display_name":"IEEE Conference Proceedings","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":"IEEE Conference Proceedings","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","score":0.5099999904632568,"display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320329860","display_name":"National Science and Technology Major Project","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W1640878532","https://openalex.org/W2026012689","https://openalex.org/W2041616772","https://openalex.org/W2082828292","https://openalex.org/W2115252128","https://openalex.org/W2116001771","https://openalex.org/W2145339207","https://openalex.org/W2163922914","https://openalex.org/W2169464261","https://openalex.org/W2173163709","https://openalex.org/W2257979135","https://openalex.org/W2404368331","https://openalex.org/W2414603974","https://openalex.org/W2616180702","https://openalex.org/W2745868649","https://openalex.org/W2766447205","https://openalex.org/W3103559770","https://openalex.org/W4298857966","https://openalex.org/W6677618333","https://openalex.org/W6697274609"],"related_works":["https://openalex.org/W4306904969","https://openalex.org/W4295532600","https://openalex.org/W2063823869","https://openalex.org/W2138720691","https://openalex.org/W4362501864","https://openalex.org/W4380318855","https://openalex.org/W3084456289","https://openalex.org/W2024136090","https://openalex.org/W2047973478","https://openalex.org/W2598946408"],"abstract_inverted_index":{"This":[0,55],"paper":[1],"presents":[2],"a":[3,35,62,114,121],"novel":[4],"deep":[5],"Reinforcement":[6],"Learning":[7],"(RL)":[8],"framework":[9,78],"for":[10,108],"classifying":[11],"movie":[12,124,144],"scenes":[13,145,149],"based":[14,79],"on":[15,80,120,139,147],"affect":[16],"using":[17],"the":[18,23,32,46,67,109,137,140,150],"face":[19],"images":[20],"detected":[21],"in":[22,98],"video":[24,33],"stream":[25],"as":[26],"input.":[27],"Extracting":[28],"affective":[29],"information":[30,53],"from":[31],"is":[34,83,116,153],"challenging":[36],"task":[37],"modulating":[38],"complex":[39,47],"visual":[40],"and":[41,52,88],"temporal":[42],"representations":[43],"intertwined":[44],"with":[45],"aspects":[48],"of":[49,69,93,112,123,136],"human":[50],"perception":[51],"integration.":[54],"also":[56],"makes":[57],"it":[58],"difficult":[59],"to":[60,85],"collect":[61],"large":[63],"annotated":[64],"corpus":[65],"restricting":[66],"use":[68,92],"supervised":[70],"learning":[71,77],"methods.":[72],"We":[73,102],"present":[74],"an":[75,99],"alternative":[76],"RL":[81,106],"that":[82,130],"tolerant":[84],"label":[86],"sparsity":[87],"can":[89],"easily":[90],"make":[91],"any":[94],"available":[95],"ground":[96],"truth":[97],"online":[100],"fashion.":[101],"employ":[103],"this":[104],"modified":[105],"model":[107,132],"binary":[110],"classification":[111],"whether":[113],"scene":[115,125],"funny":[117],"or":[118],"not":[119],"dataset":[122],"clips.":[126],"The":[127],"results":[128],"show":[129],"our":[131],"correctly":[133],"predicts":[134],"72.95%":[135],"time":[138],"2-3":[141],"minute":[142],"long":[143],"while":[146],"shorter":[148],"accuracy":[151],"obtained":[152],"84.13%.":[154]},"counts_by_year":[{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":5}],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}