{"id":"https://openalex.org/W3044002918","doi":"https://doi.org/10.1109/access.2020.3010846","title":"Residual Learning of Video Frame Interpolation Using Convolutional LSTM","display_name":"Residual Learning of Video Frame Interpolation Using Convolutional LSTM","publication_year":2020,"publication_date":"2020-01-01","ids":{"openalex":"https://openalex.org/W3044002918","doi":"https://doi.org/10.1109/access.2020.3010846","mag":"3044002918"},"language":"en","primary_location":{"id":"doi:10.1109/access.2020.3010846","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2020.3010846","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8948470/09145730.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/6287639/8948470/09145730.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5080431485","display_name":"Keito Suzuki","orcid":"https://orcid.org/0000-0002-6523-1948"},"institutions":[{"id":"https://openalex.org/I203951103","display_name":"Keio University","ror":"https://ror.org/02kn6nx58","country_code":"JP","type":"education","lineage":["https://openalex.org/I203951103"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Keito Suzuki","raw_affiliation_strings":["Department of Electronics and Electrical Engineering, Keio University, Yokohama, Japan"],"raw_orcid":"https://orcid.org/0000-0002-6523-1948","affiliations":[{"raw_affiliation_string":"Department of Electronics and Electrical Engineering, Keio University, Yokohama, Japan","institution_ids":["https://openalex.org/I203951103"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5090181402","display_name":"Masaaki Ikehara","orcid":"https://orcid.org/0000-0003-3461-1507"},"institutions":[{"id":"https://openalex.org/I203951103","display_name":"Keio University","ror":"https://ror.org/02kn6nx58","country_code":"JP","type":"education","lineage":["https://openalex.org/I203951103"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Masaaki Ikehara","raw_affiliation_strings":["Department of Electronics and Electrical Engineering, Keio University, Yokohama, Japan"],"raw_orcid":"https://orcid.org/0000-0003-3461-1507","affiliations":[{"raw_affiliation_string":"Department of Electronics and Electrical Engineering, Keio University, Yokohama, Japan","institution_ids":["https://openalex.org/I203951103"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I203951103"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.6708,"has_fulltext":true,"cited_by_count":12,"citation_normalized_percentile":{"value":0.71318147,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":"8","issue":null,"first_page":"134185","last_page":"134193"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10531","display_name":"Advanced Vision and Imaging","score":0.9998999834060669,"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/T10531","display_name":"Advanced Vision and Imaging","score":0.9998999834060669,"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":0.9997000098228455,"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/T13114","display_name":"Image Processing Techniques and Applications","score":0.9921000003814697,"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/motion-interpolation","display_name":"Motion interpolation","score":0.8742331266403198},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7951706647872925},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7091709971427917},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.6704791188240051},{"id":"https://openalex.org/keywords/optical-flow","display_name":"Optical flow","score":0.6213654279708862},{"id":"https://openalex.org/keywords/residual-frame","display_name":"Residual frame","score":0.614596426486969},{"id":"https://openalex.org/keywords/interpolation","display_name":"Interpolation (computer graphics)","score":0.6111485362052917},{"id":"https://openalex.org/keywords/frame","display_name":"Frame (networking)","score":0.5858001708984375},{"id":"https://openalex.org/keywords/motion-estimation","display_name":"Motion estimation","score":0.5729538202285767},{"id":"https://openalex.org/keywords/motion","display_name":"Motion (physics)","score":0.5371570587158203},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.524509072303772},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5115419626235962},{"id":"https://openalex.org/keywords/inter-frame","display_name":"Inter frame","score":0.45586445927619934},{"id":"https://openalex.org/keywords/motion-compensation","display_name":"Motion compensation","score":0.4376859664916992},{"id":"https://openalex.org/keywords/frame-rate","display_name":"Frame rate","score":0.42368248105049133},{"id":"https://openalex.org/keywords/block-matching-algorithm","display_name":"Block-matching algorithm","score":0.40181639790534973},{"id":"https://openalex.org/keywords/reference-frame","display_name":"Reference frame","score":0.39623814821243286},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.24766620993614197},{"id":"https://openalex.org/keywords/video-tracking","display_name":"Video tracking","score":0.1855590045452118},{"id":"https://openalex.org/keywords/video-processing","display_name":"Video processing","score":0.11795550584793091},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.09896993637084961}],"concepts":[{"id":"https://openalex.org/C72560505","wikidata":"https://www.wikidata.org/wiki/Q204510","display_name":"Motion interpolation","level":5,"score":0.8742331266403198},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7951706647872925},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7091709971427917},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6704791188240051},{"id":"https://openalex.org/C155542232","wikidata":"https://www.wikidata.org/wiki/Q736111","display_name":"Optical flow","level":3,"score":0.6213654279708862},{"id":"https://openalex.org/C204641915","wikidata":"https://www.wikidata.org/wiki/Q7315509","display_name":"Residual frame","level":4,"score":0.614596426486969},{"id":"https://openalex.org/C137800194","wikidata":"https://www.wikidata.org/wiki/Q11713455","display_name":"Interpolation (computer graphics)","level":3,"score":0.6111485362052917},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.5858001708984375},{"id":"https://openalex.org/C10161872","wikidata":"https://www.wikidata.org/wiki/Q557891","display_name":"Motion estimation","level":2,"score":0.5729538202285767},{"id":"https://openalex.org/C104114177","wikidata":"https://www.wikidata.org/wiki/Q79782","display_name":"Motion (physics)","level":2,"score":0.5371570587158203},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.524509072303772},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5115419626235962},{"id":"https://openalex.org/C39394851","wikidata":"https://www.wikidata.org/wiki/Q921594","display_name":"Inter frame","level":4,"score":0.45586445927619934},{"id":"https://openalex.org/C128840427","wikidata":"https://www.wikidata.org/wiki/Q1302174","display_name":"Motion compensation","level":2,"score":0.4376859664916992},{"id":"https://openalex.org/C3261483","wikidata":"https://www.wikidata.org/wiki/Q119565","display_name":"Frame rate","level":2,"score":0.42368248105049133},{"id":"https://openalex.org/C167510206","wikidata":"https://www.wikidata.org/wiki/Q2835824","display_name":"Block-matching algorithm","level":4,"score":0.40181639790534973},{"id":"https://openalex.org/C172849965","wikidata":"https://www.wikidata.org/wiki/Q3148875","display_name":"Reference frame","level":3,"score":0.39623814821243286},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.24766620993614197},{"id":"https://openalex.org/C202474056","wikidata":"https://www.wikidata.org/wiki/Q1931635","display_name":"Video tracking","level":3,"score":0.1855590045452118},{"id":"https://openalex.org/C65483669","wikidata":"https://www.wikidata.org/wiki/Q3536669","display_name":"Video processing","level":2,"score":0.11795550584793091},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.09896993637084961},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2020.3010846","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2020.3010846","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8948470/09145730.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:8c710509afdd4e60bb9fbf9ded554c1e","is_oa":true,"landing_page_url":"https://doaj.org/article/8c710509afdd4e60bb9fbf9ded554c1e","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","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":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 8, Pp 134185-134193 (2020)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2020.3010846","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2020.3010846","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8948470/09145730.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G5452022097","display_name":"Research on rapid and accurate image restoration by fusion of signal processing and deep learning","funder_award_id":"20K04472","funder_id":"https://openalex.org/F4320334764","funder_display_name":"Japan Society for the Promotion of Science"}],"funders":[{"id":"https://openalex.org/F4320334764","display_name":"Japan Society for the Promotion of Science","ror":"https://ror.org/00hhkn466"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3044002918.pdf","grobid_xml":"https://content.openalex.org/works/W3044002918.grobid-xml"},"referenced_works_count":41,"referenced_works":["https://openalex.org/W1485009520","https://openalex.org/W1522301498","https://openalex.org/W1578285471","https://openalex.org/W1901129140","https://openalex.org/W2064675550","https://openalex.org/W2130942839","https://openalex.org/W2147253850","https://openalex.org/W2163605009","https://openalex.org/W2194775991","https://openalex.org/W2305401973","https://openalex.org/W2331128040","https://openalex.org/W2348664362","https://openalex.org/W2475287302","https://openalex.org/W2586480386","https://openalex.org/W2604329646","https://openalex.org/W2605229288","https://openalex.org/W2752782242","https://openalex.org/W2769654144","https://openalex.org/W2794834924","https://openalex.org/W2866634454","https://openalex.org/W2884585870","https://openalex.org/W2890474520","https://openalex.org/W2904829482","https://openalex.org/W2916743882","https://openalex.org/W2935854115","https://openalex.org/W2949258649","https://openalex.org/W2962793481","https://openalex.org/W2963495494","https://openalex.org/W2963574983","https://openalex.org/W2964251418","https://openalex.org/W2973673960","https://openalex.org/W3034232500","https://openalex.org/W3102015846","https://openalex.org/W6631190155","https://openalex.org/W6639824700","https://openalex.org/W6679436768","https://openalex.org/W6684191040","https://openalex.org/W6702130928","https://openalex.org/W6735698919","https://openalex.org/W6753074096","https://openalex.org/W6753412334"],"related_works":["https://openalex.org/W1899533084","https://openalex.org/W2060632806","https://openalex.org/W2119751394","https://openalex.org/W2117565081","https://openalex.org/W2106056215","https://openalex.org/W2153984380","https://openalex.org/W1981663193","https://openalex.org/W2386311284","https://openalex.org/W4255845834","https://openalex.org/W2946348769"],"abstract_inverted_index":{"Video":[0],"frame":[1,18,26,43,67,204],"interpolation":[2,27,205],"aims":[3],"to":[4,62,76,122,149,162,175,179],"generate":[5,64],"intermediate":[6,42,100],"frames":[7,37,95,105,117,131,148],"between":[8,34,94,126],"the":[9,31,35,41,46,53,56,65,69,78,92,99,112,124,127,133,165,191,211],"original":[10],"frames.":[11],"This":[12,167],"produces":[13],"videos":[14],"with":[15],"a":[16,87],"higher":[17],"rate":[19],"and":[20,38,60,96,118,132,145],"creates":[21],"smoother":[22],"motion.":[23,47],"Many":[24],"video":[25],"methods":[28,50],"first":[29],"estimate":[30],"motion":[32,57,71,82,93,196],"vector":[33],"input":[36,147],"then":[39],"synthesizes":[40],"based":[44],"on":[45,52],"However,":[48],"these":[49,115,130],"rely":[51],"accuracy":[54],"of":[55,114,129,213],"estimation":[58,197],"step":[59],"fail":[61],"accurately":[63],"interpolated":[66],"when":[68],"estimated":[70],"vectors":[72],"are":[73,106],"inaccurate.":[74],"Therefore,":[75],"avoid":[77],"uncertainties":[79],"caused":[80],"by":[81],"estimation,":[83],"this":[84],"paper":[85],"proposes":[86],"method":[88,110,141,193],"that":[89,190],"implicitly":[90],"learns":[91],"directly":[97],"generates":[98],"frame.":[101,137],"Since":[102],"two":[103,116],"consecutive":[104],"relatively":[107],"similar,":[108],"our":[109,140,160,217],"takes":[111],"average":[113,128],"utilizes":[119],"residual":[120],"learning":[121],"learn":[123],"difference":[125],"ground":[134],"truth":[135],"middle":[136],"In":[138],"addition,":[139],"uses":[142],"Convolutional":[143],"LSTMs":[144],"four":[146],"better":[150],"incorporate":[151,156],"spatiotemporal":[152],"information.":[153],"We":[154],"also":[155],"attention":[157],"mechanisms":[158],"in":[159,216],"model":[161],"further":[163],"enhance":[164],"performance.":[166],"neural":[168],"network":[169],"can":[170,198],"be":[171],"easily":[172],"trained":[173],"end":[174,176],"without":[177,194],"difficult":[178],"obtain":[180],"data":[181],"such":[182],"as":[183],"optical":[184],"flow.":[185],"Our":[186],"experimental":[187],"results":[188],"show":[189,210],"proposed":[192,218],"explicit":[195],"perform":[199],"favorably":[200],"against":[201],"other":[202],"state-of-the-art":[203],"methods.":[206],"Further":[207],"ablation":[208],"studies":[209],"effectiveness":[212],"various":[214],"components":[215],"model.":[219]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
