{"id":"https://openalex.org/W4206760028","doi":"https://doi.org/10.1109/vcip53242.2021.9675397","title":"Learn to Look Around: Deep Reinforcement Learning Agent for Video Saliency Prediction","display_name":"Learn to Look Around: Deep Reinforcement Learning Agent for Video Saliency Prediction","publication_year":2021,"publication_date":"2021-12-05","ids":{"openalex":"https://openalex.org/W4206760028","doi":"https://doi.org/10.1109/vcip53242.2021.9675397"},"language":"en","primary_location":{"id":"doi:10.1109/vcip53242.2021.9675397","is_oa":false,"landing_page_url":"https://doi.org/10.1109/vcip53242.2021.9675397","pdf_url":null,"source":{"id":"https://openalex.org/S4363608378","display_name":"2021 International Conference on Visual Communications and Image Processing (VCIP)","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":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 International Conference on Visual Communications and Image Processing (VCIP)","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/A5003271111","display_name":"Yiran Tao","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Yiran Tao","raw_affiliation_strings":["Wuhan Univeristy, Wuhan, China"],"affiliations":[{"raw_affiliation_string":"Wuhan Univeristy, Wuhan, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5086836003","display_name":"Yaosi Hu","orcid":"https://orcid.org/0000-0003-2784-6738"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yaosi Hu","raw_affiliation_strings":["Wuhan Univeristy, Wuhan, China"],"affiliations":[{"raw_affiliation_string":"Wuhan Univeristy, Wuhan, China","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5006748765","display_name":"Zhenzhong Chen","orcid":"https://orcid.org/0000-0002-7882-1066"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhenzhong Chen","raw_affiliation_strings":["Wuhan Univeristy, Wuhan, China"],"affiliations":[{"raw_affiliation_string":"Wuhan Univeristy, Wuhan, China","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5003271111"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.18404378,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"100","issue":null,"first_page":"1","last_page":"5"},"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":0.9957000017166138,"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/T10971","display_name":"Olfactory and Sensory Function Studies","score":0.9580000042915344,"subfield":{"id":"https://openalex.org/subfields/2809","display_name":"Sensory Systems"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.8673503398895264},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8416233062744141},{"id":"https://openalex.org/keywords/salient","display_name":"Salient","score":0.7039532661437988},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6939316987991333},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.6420801281929016},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.611392617225647},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.5388535857200623},{"id":"https://openalex.org/keywords/window","display_name":"Window (computing)","score":0.49147284030914307},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.46420368552207947},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4326157569885254},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3314383625984192},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.06569892168045044}],"concepts":[{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.8673503398895264},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8416233062744141},{"id":"https://openalex.org/C2780719617","wikidata":"https://www.wikidata.org/wiki/Q1030752","display_name":"Salient","level":2,"score":0.7039532661437988},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6939316987991333},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.6420801281929016},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.611392617225647},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.5388535857200623},{"id":"https://openalex.org/C2778751112","wikidata":"https://www.wikidata.org/wiki/Q835016","display_name":"Window (computing)","level":2,"score":0.49147284030914307},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.46420368552207947},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4326157569885254},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3314383625984192},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.06569892168045044},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","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/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/vcip53242.2021.9675397","is_oa":false,"landing_page_url":"https://doi.org/10.1109/vcip53242.2021.9675397","pdf_url":null,"source":{"id":"https://openalex.org/S4363608378","display_name":"2021 International Conference on Visual Communications and Image Processing (VCIP)","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":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 International Conference on Visual Communications and Image Processing (VCIP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G879442631","display_name":null,"funder_award_id":"62036005","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":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":30,"referenced_works":["https://openalex.org/W1009312522","https://openalex.org/W1517086206","https://openalex.org/W1959094031","https://openalex.org/W1967508848","https://openalex.org/W2032007016","https://openalex.org/W2034436892","https://openalex.org/W2055111849","https://openalex.org/W2063608179","https://openalex.org/W2111997583","https://openalex.org/W2118985252","https://openalex.org/W2119228922","https://openalex.org/W2119577735","https://openalex.org/W2122710056","https://openalex.org/W2128272608","https://openalex.org/W2133589685","https://openalex.org/W2135957164","https://openalex.org/W2145339207","https://openalex.org/W2148383759","https://openalex.org/W2212216676","https://openalex.org/W2378845821","https://openalex.org/W2902418641","https://openalex.org/W2962965915","https://openalex.org/W2963828885","https://openalex.org/W2969741484","https://openalex.org/W2986131415","https://openalex.org/W3011154664","https://openalex.org/W3099561715","https://openalex.org/W6630781460","https://openalex.org/W6680437723","https://openalex.org/W6764953915"],"related_works":["https://openalex.org/W2329500892","https://openalex.org/W28991112","https://openalex.org/W2370726991","https://openalex.org/W2109115373","https://openalex.org/W2390901981","https://openalex.org/W2369710579","https://openalex.org/W4306904969","https://openalex.org/W4327728159","https://openalex.org/W4394266730","https://openalex.org/W1990856605"],"abstract_inverted_index":{"In":[0,20],"the":[1,8,12,49,70,80,83,94],"video":[2,29,117],"saliency":[3,30,56,118],"prediction":[4,31,119],"task,":[5],"one":[6],"of":[7,14,18,53,82],"key":[9],"issues":[10],"is":[11,32,90,113],"utilization":[13],"temporal":[15],"contextual":[16,45],"information":[17,52],"keyframes.":[19],"this":[21],"paper,":[22],"a":[23,43,73,98],"deep":[24,87],"reinforcement":[25],"learning":[26],"agent":[27,95,106],"for":[28,55],"proposed,":[33],"designed":[34],"to":[35,68,92,96,100],"look":[36],"around":[37],"adjacent":[38],"frames":[39],"and":[40,76],"adaptively":[41],"generate":[42],"salient":[44],"window":[46],"that":[47,127],"contains":[48],"most":[50],"correlated":[51],"keyframe":[54],"prediction.":[57],"More":[58],"specifically,":[59],"an":[60,132],"action":[61],"set":[62,75],"step":[63,65],"by":[64],"decides":[66],"whether":[67],"expand":[69],"window,":[71],"meanwhile":[72],"state":[74],"reward":[77],"function":[78],"evaluate":[79],"effectiveness":[81],"current":[84],"window.":[85],"The":[86,104],"Q-learning":[88],"algorithm":[89],"followed":[91],"train":[93],"learn":[97],"policy":[99],"achieve":[101,131],"its":[102],"goal.":[103],"proposed":[105],"can":[107,130],"be":[108],"regarded":[109],"as":[110],"plug-and-play":[111],"which":[112],"compatible":[114],"with":[115],"generic":[116],"models.":[120],"Experimental":[121],"results":[122],"on":[123],"various":[124],"datasets":[125],"demonstrate":[126],"our":[128],"method":[129],"advanced":[133],"performance.":[134]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
