{"id":"https://openalex.org/W3187148937","doi":"https://doi.org/10.1145/3474085.3475320","title":"Ada-VSR: Adaptive Video Super-Resolution with Meta-Learning","display_name":"Ada-VSR: Adaptive Video Super-Resolution with Meta-Learning","publication_year":2021,"publication_date":"2021-10-17","ids":{"openalex":"https://openalex.org/W3187148937","doi":"https://doi.org/10.1145/3474085.3475320","mag":"3187148937"},"language":"en","primary_location":{"id":"doi:10.1145/3474085.3475320","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3474085.3475320","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3474085.3475320","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 29th ACM International Conference on Multimedia","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3474085.3475320","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101643233","display_name":"Akash Gupta","orcid":"https://orcid.org/0000-0002-3752-4751"},"institutions":[{"id":"https://openalex.org/I103635307","display_name":"University of California, Riverside","ror":"https://ror.org/03nawhv43","country_code":"US","type":"education","lineage":["https://openalex.org/I103635307"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Akash Gupta","raw_affiliation_strings":["University of California, Riverside, Riverside, CA, USA","Univ. of California Riverside, Riverside, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of California, Riverside, Riverside, CA, USA","institution_ids":["https://openalex.org/I103635307"]},{"raw_affiliation_string":"Univ. of California Riverside, Riverside, CA, USA","institution_ids":["https://openalex.org/I103635307"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5040371676","display_name":"Padmaja Jonnalagedda","orcid":"https://orcid.org/0000-0002-0128-6691"},"institutions":[{"id":"https://openalex.org/I103635307","display_name":"University of California, Riverside","ror":"https://ror.org/03nawhv43","country_code":"US","type":"education","lineage":["https://openalex.org/I103635307"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Padmaja Jonnalagedda","raw_affiliation_strings":["University of California, Riverside, Riverside, CA, USA","Univ. of California Riverside, Riverside, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of California, Riverside, Riverside, CA, USA","institution_ids":["https://openalex.org/I103635307"]},{"raw_affiliation_string":"Univ. of California Riverside, Riverside, CA, USA","institution_ids":["https://openalex.org/I103635307"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5071522801","display_name":"Bir Bhanu","orcid":"https://orcid.org/0000-0001-8971-6416"},"institutions":[{"id":"https://openalex.org/I103635307","display_name":"University of California, Riverside","ror":"https://ror.org/03nawhv43","country_code":"US","type":"education","lineage":["https://openalex.org/I103635307"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Bir Bhanu","raw_affiliation_strings":["University of California, Riverside, Riverside, CA, USA","Univ. of California Riverside, Riverside, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of California, Riverside, Riverside, CA, USA","institution_ids":["https://openalex.org/I103635307"]},{"raw_affiliation_string":"Univ. of California Riverside, Riverside, CA, USA","institution_ids":["https://openalex.org/I103635307"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5004732580","display_name":"Amit K. Roy\u2013Chowdhury","orcid":"https://orcid.org/0000-0001-6690-9725"},"institutions":[{"id":"https://openalex.org/I103635307","display_name":"University of California, Riverside","ror":"https://ror.org/03nawhv43","country_code":"US","type":"education","lineage":["https://openalex.org/I103635307"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Amit K. Roy-Chowdhury","raw_affiliation_strings":["University of California, Riverside, Riverside, CA, USA","University of California, Riverside"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of California, Riverside, Riverside, CA, USA","institution_ids":["https://openalex.org/I103635307"]},{"raw_affiliation_string":"University of California, Riverside","institution_ids":["https://openalex.org/I103635307"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I103635307"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.08278505,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"327","last_page":"336"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11105","display_name":"Advanced Image Processing Techniques","score":0.9998000264167786,"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/T11105","display_name":"Advanced Image Processing Techniques","score":0.9998000264167786,"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":0.9954000115394592,"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":0.9768000245094299,"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/computer-science","display_name":"Computer science","score":0.8140366673469543},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6691005229949951},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6229742169380188},{"id":"https://openalex.org/keywords/frame","display_name":"Frame (networking)","score":0.5181671380996704},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.5066109895706177},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.47428423166275024},{"id":"https://openalex.org/keywords/frame-rate","display_name":"Frame rate","score":0.46164482831954956},{"id":"https://openalex.org/keywords/transfer-of-learning","display_name":"Transfer of learning","score":0.44025397300720215},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.4317851662635803},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.41704797744750977},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3639680743217468},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3466249108314514}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8140366673469543},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6691005229949951},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6229742169380188},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.5181671380996704},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.5066109895706177},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.47428423166275024},{"id":"https://openalex.org/C3261483","wikidata":"https://www.wikidata.org/wiki/Q119565","display_name":"Frame rate","level":2,"score":0.46164482831954956},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.44025397300720215},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.4317851662635803},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.41704797744750977},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3639680743217468},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3466249108314514},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"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":4,"locations":[{"id":"doi:10.1145/3474085.3475320","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3474085.3475320","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3474085.3475320","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 29th ACM International Conference on Multimedia","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2108.02832","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2108.02832","pdf_url":"https://arxiv.org/pdf/2108.02832","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":"","raw_type":"text"},{"id":"mag:3187148937","is_oa":true,"landing_page_url":"http://export.arxiv.org/pdf/2108.02832","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.2108.02832","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2108.02832","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":"doi:10.1145/3474085.3475320","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3474085.3475320","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3474085.3475320","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 29th ACM International Conference on Multimedia","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1639252047","display_name":null,"funder_award_id":"1664172 and 1724341","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G788425555","display_name":"RI: Small: Understanding Subtle Non-Social Facial Expressivity to Boost Learning and Computer Interaction","funder_award_id":"1911197","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G821890410","display_name":"SI2-SSI: LIMPID: Large-Scale IMage Processing Infrastructure Development","funder_award_id":"1664172","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3187148937.pdf","grobid_xml":"https://content.openalex.org/works/W3187148937.grobid-xml"},"referenced_works_count":73,"referenced_works":["https://openalex.org/W1531803620","https://openalex.org/W1885185971","https://openalex.org/W1965004103","https://openalex.org/W1979606177","https://openalex.org/W1981990039","https://openalex.org/W1996579288","https://openalex.org/W2102166818","https://openalex.org/W2114770744","https://openalex.org/W2119781527","https://openalex.org/W2133665775","https://openalex.org/W2165939075","https://openalex.org/W2167191464","https://openalex.org/W2167306329","https://openalex.org/W2210480155","https://openalex.org/W2242218935","https://openalex.org/W2305401973","https://openalex.org/W2320725294","https://openalex.org/W2430196102","https://openalex.org/W2432717477","https://openalex.org/W2472819217","https://openalex.org/W2474628748","https://openalex.org/W2557227117","https://openalex.org/W2586480386","https://openalex.org/W2604329646","https://openalex.org/W2742093937","https://openalex.org/W2766363782","https://openalex.org/W2769654144","https://openalex.org/W2784596339","https://openalex.org/W2787035179","https://openalex.org/W2798664922","https://openalex.org/W2803672301","https://openalex.org/W2808682055","https://openalex.org/W2890136477","https://openalex.org/W2899771611","https://openalex.org/W2943011330","https://openalex.org/W2949258649","https://openalex.org/W2950537964","https://openalex.org/W2951881474","https://openalex.org/W2954930822","https://openalex.org/W2962799101","https://openalex.org/W2962814024","https://openalex.org/W2962974944","https://openalex.org/W2963093735","https://openalex.org/W2963372104","https://openalex.org/W2963470893","https://openalex.org/W2963704386","https://openalex.org/W2963729050","https://openalex.org/W2963943197","https://openalex.org/W2963959907","https://openalex.org/W2964013315","https://openalex.org/W2964040059","https://openalex.org/W2964105864","https://openalex.org/W2964251418","https://openalex.org/W2964277374","https://openalex.org/W2965669158","https://openalex.org/W2970778968","https://openalex.org/W2976718572","https://openalex.org/W2983118621","https://openalex.org/W2998095399","https://openalex.org/W3012637317","https://openalex.org/W3034850705","https://openalex.org/W3035236663","https://openalex.org/W3035304632","https://openalex.org/W3046792400","https://openalex.org/W3082299968","https://openalex.org/W3092954151","https://openalex.org/W3093013777","https://openalex.org/W3102015846","https://openalex.org/W3105328221","https://openalex.org/W3118973820","https://openalex.org/W3125564923","https://openalex.org/W4235058523","https://openalex.org/W4249522571"],"related_works":["https://openalex.org/W3205254310","https://openalex.org/W3137901473","https://openalex.org/W3107505690","https://openalex.org/W3161000886","https://openalex.org/W3170713111","https://openalex.org/W3159040138","https://openalex.org/W3117544654","https://openalex.org/W3035374961","https://openalex.org/W3161313759","https://openalex.org/W2909000493","https://openalex.org/W2923850366","https://openalex.org/W3177415989","https://openalex.org/W3025556461","https://openalex.org/W3013502921","https://openalex.org/W2963526497","https://openalex.org/W2770112119","https://openalex.org/W3174779539","https://openalex.org/W3133108707","https://openalex.org/W2799209564","https://openalex.org/W3157466400"],"abstract_inverted_index":{"Most":[0],"of":[1,19,76,99,114,122,171,187],"the":[2,41,48,96,119,123,166,172,177,214],"existing":[3],"works":[4],"in":[5,60],"supervised":[6],"spatio-temporal":[7],"video":[8,43,50,78,175,189,204],"super-resolution":[9],"(STVSR)":[10],"heavily":[11],"rely":[12],"on":[13,220],"a":[14,37,52,77,100,106,157,188,202,208],"large-scale":[15,158],"external":[16,159,183],"dataset":[17],"consisting":[18],"paired":[20],"low-resolution":[21,42],"low-frame":[22],"rate":[23],"(LR-LFR)":[24],"and":[25,144,184],"high-resolution":[26,49],"high-frame-rate":[27],"(HR-HFR)":[28],"videos.":[29],"Despite":[30],"their":[31,92],"remarkable":[32],"performance,":[33],"these":[34,66,103],"methods":[35,67,104],"make":[36],"prior":[38],"assumption":[39],"that":[40,69,161,224],"is":[44,68,150],"obtained":[45],"by":[46],"down-scaling":[47],"using":[51,156,195],"known":[53],"degradation":[54],"kernel,":[55],"which":[56,133,212],"does":[57],"not":[58],"hold":[59],"practical":[61],"settings.":[62],"Another":[63],"problem":[64],"with":[65,206],"they":[70,111],"cannot":[71],"exploit":[72],"instance-specific":[73,97],"internal":[74,84,145,178,185],"information":[75,140,186],"at":[79],"testing":[80],"time.":[81],"Recently,":[82],"deep":[83],"learning":[85,143,179],"approaches":[86],"have":[87,105],"gained":[88],"attention":[89],"due":[90],"to":[91,94,117,152,165,201],"ability":[93],"utilize":[95],"statistics":[98],"video.":[101],"However,":[102],"large":[107],"inference":[108,215],"time":[109,216],"as":[110,136,138],"require":[112],"thousands":[113],"gradient":[115,210],"updates":[116],"learn":[118],"intrinsic":[120],"structure":[121],"data.":[124],"In":[125],"this":[126],"work,":[127],"we":[128],"present":[129],"Adaptive":[130],"VideoSuper-Resolution":[131],"(Ada-VSR)":[132],"leverages":[134],"external,":[135],"well":[137],"internal,":[139],"through":[141],"meta-transfer":[142],"learning,":[146],"respectively.":[147],"Specifically,":[148],"meta-learning":[149],"employed":[151],"obtain":[153],"adaptive":[154],"parameters,":[155],"dataset,":[160],"can":[162,198],"adapt":[163,200],"quickly":[164,199],"novel":[167],"condition":[168,205],"(degradation":[169],"model)":[170],"given":[173],"test":[174],"during":[176],"task,":[180],"thereby":[181],"exploiting":[182],"for":[190],"super-resolution.":[191],"The":[192],"model":[193],"trained":[194],"our":[196,225],"approach":[197],"specific":[203],"only":[207],"few":[209],"updates,":[211],"reduces":[213],"significantly.":[217],"Extensive":[218],"experiments":[219],"standard":[221],"datasets":[222],"demonstrate":[223],"method":[226],"performs":[227],"favorably":[228],"against":[229],"various":[230],"state-of-the-art":[231],"approaches.":[232]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
