{"id":"https://openalex.org/W2997641130","doi":"https://doi.org/10.1109/wacv45572.2020.9093330","title":"Personalizing Fast-Forward Videos Based on Visual and Textual Features from Social Network","display_name":"Personalizing Fast-Forward Videos Based on Visual and Textual Features from Social Network","publication_year":2020,"publication_date":"2020-03-01","ids":{"openalex":"https://openalex.org/W2997641130","doi":"https://doi.org/10.1109/wacv45572.2020.9093330","mag":"2997641130"},"language":"en","primary_location":{"id":"doi:10.1109/wacv45572.2020.9093330","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wacv45572.2020.9093330","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE Winter Conference on Applications of Computer Vision (WACV)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1912.12655","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5041965939","display_name":"Washington Ramos","orcid":"https://orcid.org/0000-0002-0411-8677"},"institutions":[{"id":"https://openalex.org/I110200422","display_name":"Universidade Federal de Minas Gerais","ror":"https://ror.org/0176yjw32","country_code":"BR","type":"education","lineage":["https://openalex.org/I110200422"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Washington L. S. Ramos","raw_affiliation_strings":["Universidade Federal de Minas Gerais (UFMG), Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Universidade Federal de Minas Gerais (UFMG), Brazil","institution_ids":["https://openalex.org/I110200422"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5090596227","display_name":"Michel M. Silva","orcid":"https://orcid.org/0000-0002-2499-9619"},"institutions":[{"id":"https://openalex.org/I110200422","display_name":"Universidade Federal de Minas Gerais","ror":"https://ror.org/0176yjw32","country_code":"BR","type":"education","lineage":["https://openalex.org/I110200422"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Michel M. Silva","raw_affiliation_strings":["Universidade Federal de Minas Gerais (UFMG), Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Universidade Federal de Minas Gerais (UFMG), Brazil","institution_ids":["https://openalex.org/I110200422"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5061048633","display_name":"Edson Araujo","orcid":"https://orcid.org/0000-0003-0585-5473"},"institutions":[{"id":"https://openalex.org/I110200422","display_name":"Universidade Federal de Minas Gerais","ror":"https://ror.org/0176yjw32","country_code":"BR","type":"education","lineage":["https://openalex.org/I110200422"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Edson R. Araujo","raw_affiliation_strings":["Universidade Federal de Minas Gerais (UFMG), Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Universidade Federal de Minas Gerais (UFMG), Brazil","institution_ids":["https://openalex.org/I110200422"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5057438046","display_name":"Alan C. Neves","orcid":null},"institutions":[{"id":"https://openalex.org/I110200422","display_name":"Universidade Federal de Minas Gerais","ror":"https://ror.org/0176yjw32","country_code":"BR","type":"education","lineage":["https://openalex.org/I110200422"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Alan C. Neves","raw_affiliation_strings":["Universidade Federal de Minas Gerais (UFMG), Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Universidade Federal de Minas Gerais (UFMG), Brazil","institution_ids":["https://openalex.org/I110200422"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5002613601","display_name":"Erickson R. Nascimento","orcid":"https://orcid.org/0000-0003-2973-2232"},"institutions":[{"id":"https://openalex.org/I110200422","display_name":"Universidade Federal de Minas Gerais","ror":"https://ror.org/0176yjw32","country_code":"BR","type":"education","lineage":["https://openalex.org/I110200422"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Erickson R. Nascimento","raw_affiliation_strings":["Universidade Federal de Minas Gerais (UFMG), Brazil","FEDERAL UNIVERSITY OF MINAS GERAIS"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Universidade Federal de Minas Gerais (UFMG), Brazil","institution_ids":["https://openalex.org/I110200422"]},{"raw_affiliation_string":"FEDERAL UNIVERSITY OF MINAS GERAIS","institution_ids":["https://openalex.org/I110200422"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I110200422"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"3260","last_page":"3269"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11439","display_name":"Video Analysis and Summarization","score":0.9976999759674072,"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/T11439","display_name":"Video Analysis and Summarization","score":0.9976999759674072,"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/T10203","display_name":"Recommender Systems and Techniques","score":0.9782000184059143,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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":0.9768999814987183,"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":0.7892293930053711},{"id":"https://openalex.org/keywords/personalization","display_name":"Personalization","score":0.7231994867324829},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.6512902975082397},{"id":"https://openalex.org/keywords/competitor-analysis","display_name":"Competitor analysis","score":0.6127381324768066},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.5986754298210144},{"id":"https://openalex.org/keywords/habit","display_name":"Habit","score":0.5028373599052429},{"id":"https://openalex.org/keywords/social-network","display_name":"Social network (sociolinguistics)","score":0.4973461925983429},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.44696518778800964},{"id":"https://openalex.org/keywords/schema-crosswalk","display_name":"Schema crosswalk","score":0.42969390749931335},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.42487239837646484},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4158400297164917},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.39606475830078125},{"id":"https://openalex.org/keywords/world-wide-web","display_name":"World Wide Web","score":0.376811683177948},{"id":"https://openalex.org/keywords/social-media","display_name":"Social media","score":0.24501267075538635},{"id":"https://openalex.org/keywords/computer-security","display_name":"Computer security","score":0.1583368182182312}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7892293930053711},{"id":"https://openalex.org/C183003079","wikidata":"https://www.wikidata.org/wiki/Q1000371","display_name":"Personalization","level":2,"score":0.7231994867324829},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.6512902975082397},{"id":"https://openalex.org/C127576917","wikidata":"https://www.wikidata.org/wiki/Q624630","display_name":"Competitor analysis","level":2,"score":0.6127381324768066},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.5986754298210144},{"id":"https://openalex.org/C44670240","wikidata":"https://www.wikidata.org/wiki/Q1299714","display_name":"Habit","level":2,"score":0.5028373599052429},{"id":"https://openalex.org/C4727928","wikidata":"https://www.wikidata.org/wiki/Q17164759","display_name":"Social network (sociolinguistics)","level":3,"score":0.4973461925983429},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.44696518778800964},{"id":"https://openalex.org/C121193887","wikidata":"https://www.wikidata.org/wiki/Q7431117","display_name":"Schema crosswalk","level":3,"score":0.42969390749931335},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.42487239837646484},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4158400297164917},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.39606475830078125},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.376811683177948},{"id":"https://openalex.org/C518677369","wikidata":"https://www.wikidata.org/wiki/Q202833","display_name":"Social media","level":2,"score":0.24501267075538635},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.1583368182182312},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","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},{"id":"https://openalex.org/C542102704","wikidata":"https://www.wikidata.org/wiki/Q183257","display_name":"Psychotherapist","level":1,"score":0.0},{"id":"https://openalex.org/C22212356","wikidata":"https://www.wikidata.org/wiki/Q775325","display_name":"Transport engineering","level":1,"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/C2777113093","wikidata":"https://www.wikidata.org/wiki/Q221488","display_name":"Pedestrian","level":2,"score":0.0},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1109/wacv45572.2020.9093330","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wacv45572.2020.9093330","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE Winter Conference on Applications of Computer Vision (WACV)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:1912.12655","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1912.12655","pdf_url":"https://arxiv.org/pdf/1912.12655","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"mag:2997641130","is_oa":true,"landing_page_url":"https://arxiv.org/pdf/1912.12655.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},{"id":"doi:10.48550/arxiv.1912.12655","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.1912.12655","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1912.12655","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1912.12655","pdf_url":"https://arxiv.org/pdf/1912.12655","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education","score":0.4300000071525574}],"awards":[],"funders":[{"id":"https://openalex.org/F4320309480","display_name":"Nvidia","ror":"https://ror.org/03jdj4y14"},{"id":"https://openalex.org/F4320321091","display_name":"Coordena\u00e7\u00e3o de Aperfei\u00e7oamento de Pessoal de N\u00edvel Superior","ror":"https://ror.org/00x0ma614"},{"id":"https://openalex.org/F4320322025","display_name":"Conselho Nacional de Desenvolvimento Cient\u00edfico e Tecnol\u00f3gico","ror":"https://ror.org/03swz6y49"},{"id":"https://openalex.org/F4320322980","display_name":"Funda\u00e7\u00e3o de Amparo \u00e0 Pesquisa do Estado de Minas Gerais","ror":"https://ror.org/00nc55f03"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2997641130.pdf","grobid_xml":"https://content.openalex.org/works/W2997641130.grobid-xml"},"referenced_works_count":50,"referenced_works":["https://openalex.org/W1931639407","https://openalex.org/W1948812921","https://openalex.org/W1978990080","https://openalex.org/W2003553461","https://openalex.org/W2005311637","https://openalex.org/W2021363300","https://openalex.org/W2048783874","https://openalex.org/W2056609571","https://openalex.org/W2106229755","https://openalex.org/W2107034620","https://openalex.org/W2116235440","https://openalex.org/W2125215748","https://openalex.org/W2139501017","https://openalex.org/W2153579005","https://openalex.org/W2212494831","https://openalex.org/W2277195237","https://openalex.org/W2463955103","https://openalex.org/W2467794422","https://openalex.org/W2481240925","https://openalex.org/W2508429489","https://openalex.org/W2515191187","https://openalex.org/W2517959782","https://openalex.org/W2518952733","https://openalex.org/W2570848768","https://openalex.org/W2604274373","https://openalex.org/W2610201393","https://openalex.org/W2613031639","https://openalex.org/W2616324828","https://openalex.org/W2753311918","https://openalex.org/W2753900927","https://openalex.org/W2757028014","https://openalex.org/W2788106261","https://openalex.org/W2793694687","https://openalex.org/W2797938814","https://openalex.org/W2798554470","https://openalex.org/W2800729752","https://openalex.org/W2963037989","https://openalex.org/W2963418282","https://openalex.org/W2963508075","https://openalex.org/W2963676207","https://openalex.org/W2963758027","https://openalex.org/W3098401379","https://openalex.org/W3098465165","https://openalex.org/W3099672775","https://openalex.org/W3102887392","https://openalex.org/W3104866189","https://openalex.org/W4239946314","https://openalex.org/W6675954300","https://openalex.org/W6682691769","https://openalex.org/W6688426012"],"related_works":["https://openalex.org/W3024615975","https://openalex.org/W2931041635","https://openalex.org/W1972731019","https://openalex.org/W3002332655","https://openalex.org/W2620081833","https://openalex.org/W2950152871","https://openalex.org/W2584960826","https://openalex.org/W2341521930","https://openalex.org/W2897676901","https://openalex.org/W3198625251","https://openalex.org/W2020600820","https://openalex.org/W2302504445","https://openalex.org/W3048052215","https://openalex.org/W3124851460","https://openalex.org/W1852009099","https://openalex.org/W2963745622","https://openalex.org/W2036125087","https://openalex.org/W2941794368","https://openalex.org/W2951914833","https://openalex.org/W2133158939"],"abstract_inverted_index":{"The":[0],"growth":[1],"of":[2,9,22,51,65,88,104,142,163],"Social":[3],"Networks":[4],"has":[5],"fueled":[6],"the":[7,23,37,49,86,92,110,150,161],"habit":[8],"people":[10],"logging":[11],"their":[12],"day-to-day":[13],"activities,":[14],"and":[15,40,106,127],"long":[16],"First-Person":[17],"Videos":[18],"(FPVs)":[19],"are":[20,33,119],"one":[21],"main":[24],"tools":[25],"in":[26,63],"this":[27,68],"new":[28,73],"habit.":[29],"Semantic-aware":[30],"fast-forward":[31,79],"methods":[32,58],"able":[34],"to":[35,47,75,100,109,114,144,159],"decrease":[36],"watch":[38],"time":[39],"select":[41],"meaningful":[42],"moments,":[43],"which":[44],"is":[45],"key":[46],"increase":[48],"chances":[50],"these":[52,57],"videos":[53,80],"being":[54],"watched.":[55],"However,":[56],"can":[59],"not":[60],"handle":[61],"semantics":[62],"terms":[64],"personalization.":[66],"In":[67],"paper,":[69],"we":[70],"present":[71,155],"a":[72,156],"approach":[74,84],"automatically":[76],"creating":[77],"personalized":[78],"for":[81],"FPVs.":[82],"Our":[83,132],"explores":[85],"availability":[87],"text-centric":[89],"data":[90],"from":[91],"user's":[93],"social":[94],"networks":[95],"such":[96],"as":[97,130],"status":[98],"updates":[99],"infer":[101],"her/his":[102,115],"topics":[103],"interest":[105],"assigns":[107],"scores":[108],"input":[111],"frames":[112],"according":[113],"preferences.":[116],"Extensive":[117],"experiments":[118],"conducted":[120],"on":[121],"three":[122],"different":[123],"datasets":[124],"with":[125],"simulated":[126],"real-world":[128],"users":[129],"input.":[131],"method":[133],"achieved":[134],"an":[135],"average":[136],"F":[137],"<sub":[138],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[139],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">1</sub>":[140],"score":[141],"up":[143],"12.8":[145],"percentage":[146],"points":[147],"higher":[148],"than":[149],"best":[151],"competitors.":[152],"We":[153],"also":[154],"user":[157],"study":[158],"demonstrate":[160],"effectiveness":[162],"our":[164],"method.":[165]},"counts_by_year":[{"year":2022,"cited_by_count":1}],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2025-10-10T00:00:00"}