{"id":"https://openalex.org/W3034292625","doi":"https://doi.org/10.1109/icme46284.2020.9102908","title":"Multimodal Video Saliency Analysis With User-Biased Information","display_name":"Multimodal Video Saliency Analysis With User-Biased Information","publication_year":2020,"publication_date":"2020-06-09","ids":{"openalex":"https://openalex.org/W3034292625","doi":"https://doi.org/10.1109/icme46284.2020.9102908","mag":"3034292625"},"language":"en","primary_location":{"id":"doi:10.1109/icme46284.2020.9102908","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icme46284.2020.9102908","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Conference on Multimedia and Expo (ICME)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5002836842","display_name":"Jiangyue Xia","orcid":null},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiangyue Xia","raw_affiliation_strings":["Tsinghua University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021905697","display_name":"Jingqi Tian","orcid":null},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jingqi Tian","raw_affiliation_strings":["Tsinghua University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100648567","display_name":"Hui Qiao","orcid":"https://orcid.org/0000-0002-4896-8657"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hui Qiao","raw_affiliation_strings":["Tsinghua University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100426730","display_name":"Yichen Li","orcid":"https://orcid.org/0009-0009-8370-644X"},"institutions":[{"id":"https://openalex.org/I92403157","display_name":"University of Science and Technology Beijing","ror":"https://ror.org/02egmk993","country_code":"CN","type":"education","lineage":["https://openalex.org/I92403157"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yichen Li","raw_affiliation_strings":["University of Science and Technology Beijing"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Science and Technology Beijing","institution_ids":["https://openalex.org/I92403157"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103560215","display_name":"Jiangtao Wen","orcid":null},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiangtao Wen","raw_affiliation_strings":["Tsinghua University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101366375","display_name":"Yuxing Han","orcid":null},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuxing Han","raw_affiliation_strings":["Research Institute of Tsinghua University in Shenzhen"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Research Institute of Tsinghua University in Shenzhen","institution_ids":["https://openalex.org/I99065089"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11605","display_name":"Visual Attention and Saliency Detection","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/T11605","display_name":"Visual Attention and Saliency Detection","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/T11165","display_name":"Image and Video Quality Assessment","score":0.9979000091552734,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.954200029373169,"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":0.8663148880004883},{"id":"https://openalex.org/keywords/popularity","display_name":"Popularity","score":0.6270909905433655},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5870787501335144},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5671609044075012},{"id":"https://openalex.org/keywords/visualization","display_name":"Visualization","score":0.5338597893714905},{"id":"https://openalex.org/keywords/motion","display_name":"Motion (physics)","score":0.463817298412323},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.33043450117111206},{"id":"https://openalex.org/keywords/human\u2013computer-interaction","display_name":"Human\u2013computer interaction","score":0.32098448276519775}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8663148880004883},{"id":"https://openalex.org/C2780586970","wikidata":"https://www.wikidata.org/wiki/Q1357284","display_name":"Popularity","level":2,"score":0.6270909905433655},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5870787501335144},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5671609044075012},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.5338597893714905},{"id":"https://openalex.org/C104114177","wikidata":"https://www.wikidata.org/wiki/Q79782","display_name":"Motion (physics)","level":2,"score":0.463817298412323},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.33043450117111206},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.32098448276519775},{"id":"https://openalex.org/C77805123","wikidata":"https://www.wikidata.org/wiki/Q161272","display_name":"Social psychology","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icme46284.2020.9102908","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icme46284.2020.9102908","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Conference on Multimedia and Expo (ICME)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","score":0.41999998688697815,"display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W764651262","https://openalex.org/W1485009520","https://openalex.org/W1567708943","https://openalex.org/W1906419545","https://openalex.org/W1992855619","https://openalex.org/W2169561119","https://openalex.org/W2212216676","https://openalex.org/W2583180462","https://openalex.org/W2740797387","https://openalex.org/W2752782242","https://openalex.org/W2804567257","https://openalex.org/W2955060956","https://openalex.org/W2963274633","https://openalex.org/W2963420686","https://openalex.org/W2963503775","https://openalex.org/W2963581854","https://openalex.org/W2963920562","https://openalex.org/W3099561715","https://openalex.org/W6628877408","https://openalex.org/W6684745935","https://openalex.org/W6732940373"],"related_works":["https://openalex.org/W2368605798","https://openalex.org/W2518037665","https://openalex.org/W2348524959","https://openalex.org/W2477036161","https://openalex.org/W2368049389","https://openalex.org/W2384861574","https://openalex.org/W4294565801","https://openalex.org/W2170801710","https://openalex.org/W2952704802","https://openalex.org/W2741781807"],"abstract_inverted_index":{"Video":[0],"saliency":[1,34,60,104,117],"is":[2],"widely":[3],"used":[4],"in":[5],"various":[6],"video":[7,59,103],"understanding":[8],"and":[9,40,82],"processing":[10],"related":[11],"applications.":[12],"Despite":[13],"the":[14,20,101,109],"fact":[15],"that":[16,97],"studies":[17],"have":[18,41],"indicated":[19],"influence":[21],"of":[22,111],"user":[23],"preferences":[24],"on":[25,33,37],"visual":[26,38],"attention":[27,71],"when":[28],"watching":[29],"videos,":[30],"current":[31],"researches":[32],"are":[35],"based":[36],"contents":[39],"not":[42],"taken":[43],"viewer-related":[44],"information":[45,115],"into":[46,73,116],"account.":[47],"In":[48],"this":[49],"paper,":[50],"we":[51],"propose":[52],"a":[53,68,74,84],"learning-based":[54],"multimodal":[55],"framework":[56],"to":[57,78,91],"predict":[58],"aided":[61],"by":[62],"social":[63],"data":[64],"analysis.":[65],"We":[66],"introduce":[67],"popularity":[69],"assisted":[70],"mechanism":[72],"content-specific":[75],"neural":[76],"network":[77,90],"extract":[79],"spatio-motion":[80],"features,":[81],"utilize":[83],"convolutional":[85],"long":[86],"short-term":[87],"memory":[88],"(ConvLSTM)":[89],"discover":[92],"temporal":[93],"characteristics.":[94],"Experiments":[95],"demonstrate":[96],"our":[98],"approach":[99],"outperforms":[100],"state-of-the-art":[102],"analysis":[105],"methods,":[106],"which":[107],"validates":[108],"effectiveness":[110],"incorporating":[112],"external":[113],"user-biased":[114],"prediction.":[118]},"counts_by_year":[{"year":2022,"cited_by_count":2}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}