{"id":"https://openalex.org/W3206318420","doi":"https://doi.org/10.1145/3474085.3475183","title":"PUGCQ: A Large Scale Dataset for Quality Assessment of Professional User-Generated Content","display_name":"PUGCQ: A Large Scale Dataset for Quality Assessment of Professional User-Generated Content","publication_year":2021,"publication_date":"2021-10-17","ids":{"openalex":"https://openalex.org/W3206318420","doi":"https://doi.org/10.1145/3474085.3475183","mag":"3206318420"},"language":"en","primary_location":{"id":"doi:10.1145/3474085.3475183","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3474085.3475183","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 29th ACM International Conference on Multimedia","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/A5013643897","display_name":"Li Guo","orcid":"https://orcid.org/0000-0003-3821-4058"},"institutions":[{"id":"https://openalex.org/I4210108461","display_name":"Kingsoft (China)","ror":"https://ror.org/01stnfn33","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210108461"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Guo Li","raw_affiliation_strings":["Kingsoft Cloud, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Kingsoft Cloud, Beijing, China","institution_ids":["https://openalex.org/I4210108461"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5063776325","display_name":"Baoliang Chen","orcid":"https://orcid.org/0000-0003-4884-6956"},"institutions":[{"id":"https://openalex.org/I168719708","display_name":"City University of Hong Kong","ror":"https://ror.org/03q8dnn23","country_code":"HK","type":"education","lineage":["https://openalex.org/I168719708"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Baoliang Chen","raw_affiliation_strings":["City University of Hong Kong, Hong Kong, China"],"affiliations":[{"raw_affiliation_string":"City University of Hong Kong, Hong Kong, China","institution_ids":["https://openalex.org/I168719708"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5045637729","display_name":"Lingyu Zhu","orcid":"https://orcid.org/0000-0001-7608-7913"},"institutions":[{"id":"https://openalex.org/I168719708","display_name":"City University of Hong Kong","ror":"https://ror.org/03q8dnn23","country_code":"HK","type":"education","lineage":["https://openalex.org/I168719708"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Lingyu Zhu","raw_affiliation_strings":["City University of Hong Kong, Hong Kong, China"],"affiliations":[{"raw_affiliation_string":"City University of Hong Kong, Hong Kong, China","institution_ids":["https://openalex.org/I168719708"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015360518","display_name":"Qinwen He","orcid":null},"institutions":[{"id":"https://openalex.org/I4210108461","display_name":"Kingsoft (China)","ror":"https://ror.org/01stnfn33","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210108461"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qinwen He","raw_affiliation_strings":["Kingsoft Cloud, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Kingsoft Cloud, Beijing, China","institution_ids":["https://openalex.org/I4210108461"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102740478","display_name":"Hongfei Fan","orcid":"https://orcid.org/0000-0002-3212-3967"},"institutions":[{"id":"https://openalex.org/I4210108461","display_name":"Kingsoft (China)","ror":"https://ror.org/01stnfn33","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210108461"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hongfei Fan","raw_affiliation_strings":["Kingsoft Cloud, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Kingsoft Cloud, Beijing, China","institution_ids":["https://openalex.org/I4210108461"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100385178","display_name":"Shiqi Wang","orcid":"https://orcid.org/0000-0002-3583-959X"},"institutions":[{"id":"https://openalex.org/I168719708","display_name":"City University of Hong Kong","ror":"https://ror.org/03q8dnn23","country_code":"HK","type":"education","lineage":["https://openalex.org/I168719708"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Shiqi Wang","raw_affiliation_strings":["City University of Hong Kong, Hong Kong, China"],"affiliations":[{"raw_affiliation_string":"City University of Hong Kong, Hong Kong, China","institution_ids":["https://openalex.org/I168719708"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5013643897"],"corresponding_institution_ids":["https://openalex.org/I4210108461"],"apc_list":null,"apc_paid":null,"fwci":0.1921,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.49841503,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"3728","last_page":"3736"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11165","display_name":"Image and Video Quality Assessment","score":1.0,"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"}},"topics":[{"id":"https://openalex.org/T11165","display_name":"Image and Video Quality Assessment","score":1.0,"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/T11605","display_name":"Visual Attention and Saliency Detection","score":0.9937999844551086,"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/T11659","display_name":"Advanced Image Fusion Techniques","score":0.9837999939918518,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8435980081558228},{"id":"https://openalex.org/keywords/impromptu","display_name":"Impromptu","score":0.7698778510093689},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5291376709938049},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.4947061240673065},{"id":"https://openalex.org/keywords/mobile-device","display_name":"Mobile device","score":0.4937842786312103},{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.46580368280410767},{"id":"https://openalex.org/keywords/video-quality","display_name":"Video quality","score":0.4543165862560272},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4497976005077362},{"id":"https://openalex.org/keywords/multimedia","display_name":"Multimedia","score":0.4342609643936157},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.4206297993659973},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.38701727986335754},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.3654620051383972},{"id":"https://openalex.org/keywords/world-wide-web","display_name":"World Wide Web","score":0.12433162331581116}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8435980081558228},{"id":"https://openalex.org/C2781454322","wikidata":"https://www.wikidata.org/wiki/Q6007730","display_name":"Impromptu","level":2,"score":0.7698778510093689},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5291376709938049},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.4947061240673065},{"id":"https://openalex.org/C186967261","wikidata":"https://www.wikidata.org/wiki/Q5082128","display_name":"Mobile device","level":2,"score":0.4937842786312103},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.46580368280410767},{"id":"https://openalex.org/C103910844","wikidata":"https://www.wikidata.org/wiki/Q2631256","display_name":"Video quality","level":3,"score":0.4543165862560272},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4497976005077362},{"id":"https://openalex.org/C49774154","wikidata":"https://www.wikidata.org/wiki/Q131765","display_name":"Multimedia","level":1,"score":0.4342609643936157},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.4206297993659973},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.38701727986335754},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.3654620051383972},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.12433162331581116},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"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/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","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/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3474085.3475183","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3474085.3475183","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 29th ACM International Conference on Multimedia","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9","score":0.4699999988079071}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":49,"referenced_works":["https://openalex.org/W1580389772","https://openalex.org/W1966566667","https://openalex.org/W1969235627","https://openalex.org/W1982471090","https://openalex.org/W1987931295","https://openalex.org/W2009272644","https://openalex.org/W2017336566","https://openalex.org/W2021732807","https://openalex.org/W2063658831","https://openalex.org/W2064675550","https://openalex.org/W2082964377","https://openalex.org/W2102166818","https://openalex.org/W2106517536","https://openalex.org/W2108598243","https://openalex.org/W2117535912","https://openalex.org/W2133665775","https://openalex.org/W2157454221","https://openalex.org/W2157490214","https://openalex.org/W2187089797","https://openalex.org/W2194363988","https://openalex.org/W2194775991","https://openalex.org/W2250384498","https://openalex.org/W2274287116","https://openalex.org/W2298501833","https://openalex.org/W2411063280","https://openalex.org/W2549139847","https://openalex.org/W2556068545","https://openalex.org/W2563786098","https://openalex.org/W2611434713","https://openalex.org/W2617811665","https://openalex.org/W2786672974","https://openalex.org/W2791258091","https://openalex.org/W2888928533","https://openalex.org/W2891604443","https://openalex.org/W2895868297","https://openalex.org/W2914658805","https://openalex.org/W2939995367","https://openalex.org/W2950154603","https://openalex.org/W2965644659","https://openalex.org/W2970478035","https://openalex.org/W3002992380","https://openalex.org/W3017136408","https://openalex.org/W3036643051","https://openalex.org/W3091249416","https://openalex.org/W3093431261","https://openalex.org/W3099047215","https://openalex.org/W3100498948","https://openalex.org/W3103745061","https://openalex.org/W3167030277"],"related_works":["https://openalex.org/W2241334379","https://openalex.org/W2779947354","https://openalex.org/W2583925611","https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3133861977","https://openalex.org/W2951211570","https://openalex.org/W3103566983","https://openalex.org/W3167935049","https://openalex.org/W3029198973"],"abstract_inverted_index":{"Recent":[0],"years":[1],"have":[2],"witnessed":[3],"a":[4,58,81,137,146,163],"surge":[5],"of":[6,19,76,84,139],"professional":[7,45],"user-generated":[8,173],"content":[9],"(PUGC)":[10],"based":[11,102,150,180],"video":[12,20,140],"services,":[13],"coinciding":[14],"with":[15,57,88,136,193],"the":[16,69,73,94,99,106,110,116,128,132,168,171,210],"accelerated":[17],"proliferation":[18],"acquisition":[21],"devices":[22],"such":[23],"as":[24],"mobile":[25],"phones,":[26],"wearable":[27],"cameras,":[28],"and":[29,52,79,124,145,170,202,209],"unmanned":[30],"aerial":[31],"vehicles.":[32],"Different":[33],"from":[34],"traditional":[35,172],"UGC":[36],"videos":[37,42,78,87],"by":[38,44],"impromptu":[39],"shooting,":[40],"PUGC":[41,77,86,134,169,189],"produced":[43],"users":[46],"tend":[47],"to":[48,130],"be":[49],"carefully":[50],"designed":[51],"edited,":[53],"receiving":[54],"high":[55,200],"popularity":[56],"relatively":[59],"satisfactory":[60],"playing":[61],"count.":[62],"In":[63,91],"this":[64],"paper,":[65],"we":[66,97],"systematically":[67],"conduct":[68],"comprehensive":[70],"study":[71],"on":[72,151,186],"perceptual":[74,205],"quality":[75,118,141,190],"introduce":[80],"database":[82,135],"consisting":[83],"10,000":[85],"subjective":[89,95],"ratings.":[90],"particular,":[92],"during":[93],"testing,":[96],"collect":[98],"human":[100],"opinions":[101],"upon":[103],"not":[104],"only":[105],"MOS,":[107],"but":[108],"also":[109],"attributes":[111],"that":[112],"could":[113],"potentially":[114],"influence":[115],"visual":[117],"including":[119,196],"face,":[120],"noise,":[121],"blur,":[122],"brightness,":[123],"color.":[125],"We":[126],"make":[127],"attempt":[129],"analyze":[131],"large-scale":[133],"series":[138],"assessment":[142,191],"(VQA)":[143],"algorithms":[144,192],"dedicated":[147],"baseline":[148],"model":[149],"pretrained":[152],"deep":[153],"neural":[154],"network":[155],"is":[156],"further":[157],"presented.":[158],"The":[159,207],"cross-dataset":[160],"experiments":[161],"reveal":[162],"large":[164],"domain":[165],"gap":[166],"between":[167],"videos,":[174],"which":[175],"are":[176,212],"critical":[177],"in":[178,204],"learning":[179],"VQA.":[181],"These":[182],"results":[183],"shed":[184],"light":[185],"developing":[187],"next-generation":[188],"desired":[194],"properties":[195],"promising":[197],"generalization":[198],"capability,":[199],"accuracy,":[201],"effectiveness":[203],"optimization.":[206],"dataset":[208],"codes":[211],"released":[213],"at":[214],"https://github.com/wlkdb/pugcq_create.":[215]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":3},{"year":2022,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}