{"id":"https://openalex.org/W4411584332","doi":"https://doi.org/10.1109/msn63567.2024.00098","title":"Towards Efficient Video Inpainting Based on Implicit Neural Representation","display_name":"Towards Efficient Video Inpainting Based on Implicit Neural Representation","publication_year":2024,"publication_date":"2024-12-20","ids":{"openalex":"https://openalex.org/W4411584332","doi":"https://doi.org/10.1109/msn63567.2024.00098"},"language":"en","primary_location":{"id":"doi:10.1109/msn63567.2024.00098","is_oa":false,"landing_page_url":"https://doi.org/10.1109/msn63567.2024.00098","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 20th International Conference on Mobility, Sensing and Networking (MSN)","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/A5101394155","display_name":"Shuxuan Fu","orcid":null},"institutions":[{"id":"https://openalex.org/I153473198","display_name":"North China Electric Power University","ror":"https://ror.org/04qr5t414","country_code":"CN","type":"education","lineage":["https://openalex.org/I153473198"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Shuxuan Fu","raw_affiliation_strings":["School of Mathematics and Physics, North China Electric Power University,Beijing,China"],"affiliations":[{"raw_affiliation_string":"School of Mathematics and Physics, North China Electric Power University,Beijing,China","institution_ids":["https://openalex.org/I153473198"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100311070","display_name":"Yuxin Xi","orcid":null},"institutions":[{"id":"https://openalex.org/I25254941","display_name":"Beijing Normal University","ror":"https://ror.org/022k4wk35","country_code":"CN","type":"education","lineage":["https://openalex.org/I25254941"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuxin Xi","raw_affiliation_strings":["School of Artificial Intelligence, Beijing Normal University,Beijing,China"],"affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, Beijing Normal University,Beijing,China","institution_ids":["https://openalex.org/I25254941"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5105346396","display_name":"Fangming Jing","orcid":null},"institutions":[{"id":"https://openalex.org/I4210098389","display_name":"Ministry of Civil Affairs","ror":"https://ror.org/00rmvjy83","country_code":"CN","type":"government","lineage":["https://openalex.org/I4210098389","https://openalex.org/I4210127390"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fangming Jing","raw_affiliation_strings":["Information Center of the Ministry of Civil Affairs,Data Asset Department,Beijing,China"],"affiliations":[{"raw_affiliation_string":"Information Center of the Ministry of Civil Affairs,Data Asset Department,Beijing,China","institution_ids":["https://openalex.org/I4210098389"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100378385","display_name":"Jing Wang","orcid":"https://orcid.org/0000-0001-8553-7824"},"institutions":[{"id":"https://openalex.org/I25254941","display_name":"Beijing Normal University","ror":"https://ror.org/022k4wk35","country_code":"CN","type":"education","lineage":["https://openalex.org/I25254941"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jing Wang","raw_affiliation_strings":["School of Artificial Intelligence, Beijing Normal University,Beijing,China"],"affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, Beijing Normal University,Beijing,China","institution_ids":["https://openalex.org/I25254941"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5101394155"],"corresponding_institution_ids":["https://openalex.org/I153473198"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.31643851,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"689","last_page":"697"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.9902999997138977,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.9902999997138977,"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/T11105","display_name":"Advanced Image Processing Techniques","score":0.9811999797821045,"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/T10531","display_name":"Advanced Vision and Imaging","score":0.9628999829292297,"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/inpainting","display_name":"Inpainting","score":0.9268198013305664},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7321071028709412},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.605088472366333},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5753223896026611},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.49886560440063477},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.3051973879337311}],"concepts":[{"id":"https://openalex.org/C11727466","wikidata":"https://www.wikidata.org/wiki/Q1628157","display_name":"Inpainting","level":3,"score":0.9268198013305664},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7321071028709412},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.605088472366333},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5753223896026611},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.49886560440063477},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3051973879337311},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/msn63567.2024.00098","is_oa":false,"landing_page_url":"https://doi.org/10.1109/msn63567.2024.00098","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 20th International Conference on Mobility, Sensing and Networking (MSN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1764860738","display_name":null,"funder_award_id":"62071069","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":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W2511458122","https://openalex.org/W2982006683","https://openalex.org/W3031546776","https://openalex.org/W3034968345","https://openalex.org/W3174752334","https://openalex.org/W3176368002","https://openalex.org/W3184952021","https://openalex.org/W3203570626","https://openalex.org/W4200150166","https://openalex.org/W4312280420","https://openalex.org/W4386065618","https://openalex.org/W4386076493","https://openalex.org/W4386598253","https://openalex.org/W4387986864","https://openalex.org/W4389538565","https://openalex.org/W4390872458","https://openalex.org/W4400909485","https://openalex.org/W4400909940","https://openalex.org/W4402727532","https://openalex.org/W6791346067","https://openalex.org/W6797770180","https://openalex.org/W6803493554","https://openalex.org/W6811329210","https://openalex.org/W6846281300","https://openalex.org/W6846436217","https://openalex.org/W6849983003","https://openalex.org/W6850224944","https://openalex.org/W6853577868","https://openalex.org/W6869481258"],"related_works":["https://openalex.org/W2135359786","https://openalex.org/W2772917594","https://openalex.org/W2036807459","https://openalex.org/W2058170566","https://openalex.org/W2755342338","https://openalex.org/W2166024367","https://openalex.org/W3116076068","https://openalex.org/W2229312674","https://openalex.org/W2951359407","https://openalex.org/W2079911747"],"abstract_inverted_index":{"Video":[0],"inpainting":[1,15,54,64,196,203,215],"is":[2,85,161,204],"an":[3,38],"important":[4],"method":[5,31,133],"to":[6,68,149],"improve":[7,47],"video":[8,14,43,53,62,66,81,115,124,139,183,195,202],"quality":[9],"and":[10,25,50,63,118],"viewing":[11],"experience.":[12],"Traditional":[13],"methods":[16],"require":[17],"manual":[18],"design,":[19],"which":[20,109,143],"has":[21,189],"high":[22],"operation":[23],"costs":[24],"low":[26],"efficiency.":[27],"The":[28],"deep":[29],"learning":[30,57],"based":[32],"on":[33],"implicit":[34,135],"neural":[35,116,136,168,184],"representation":[36,117,137],"provides":[37],"efficient":[39],"automatic":[40],"solution":[41],"for":[42,106,138],"inpainting.":[44,125],"It":[45],"can":[46,97],"the":[48,58,61,65,72,75,79,83,90,93,95,107,112,121,151,154,157,164,172,182,199],"accuracy":[49],"effect":[51,122],"of":[52,60,74,78,92,103,114,123,134,153,167],"by":[55,82],"automatically":[56],"characteristics":[59],"according":[67],"these":[69],"characteristics.":[70],"However,":[71],"scale":[73],"embedding":[76,96,147],"(feature)":[77],"encoded":[80],"encoder":[84],"often":[86],"small":[87,101],"(16\u00d72\u00d74).":[88],"As":[89],"input":[91],"decoder,":[94,108],"only":[98],"provide":[99],"a":[100,131,145,210],"amount":[102],"feature":[104,174],"information":[105,152],"greatly":[110],"restricts":[111],"ability":[113],"further":[119,170],"affects":[120],"In":[126],"this":[127,187],"work,":[128],"we":[129],"construct":[130],"novel":[132],"inpainting,":[140],"named":[141],"I-NeRV,":[142],"enables":[144],"large-scale":[146],"(16\u00d78\u00d716)":[148],"enrich":[150],"embedding.":[155],"Meanwhile,":[156],"random":[158],"mask":[159],"mechanism":[160],"integrated":[162],"into":[163],"coding":[165],"part":[166],"representation,":[169],"improving":[171],"network":[173,185],"extraction":[175],"ability.":[176],"Comprehensive":[177],"experiments":[178],"have":[179],"proved":[180],"that":[181],"in":[186,194,214],"paper":[188],"achieved":[190],"significant":[191],"optimization":[192],"results":[193],"tasks.":[197],"On":[198],"representative":[200],"dataset,":[201],"more":[205],"effective":[206],"than":[207],"SOTA,":[208],"with":[209],"3.47":[211],"PSNR":[212],"improvement":[213],"metrics.":[216]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
