{"id":"https://openalex.org/W4293519343","doi":"https://doi.org/10.1109/icme52920.2022.9859923","title":"Training Super-Resolution Network with Difficulty-Based Adaptive Sampling","display_name":"Training Super-Resolution Network with Difficulty-Based Adaptive Sampling","publication_year":2022,"publication_date":"2022-07-18","ids":{"openalex":"https://openalex.org/W4293519343","doi":"https://doi.org/10.1109/icme52920.2022.9859923"},"language":"en","primary_location":{"id":"doi:10.1109/icme52920.2022.9859923","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icme52920.2022.9859923","pdf_url":null,"source":{"id":"https://openalex.org/S4363607799","display_name":"2022 IEEE International Conference on Multimedia and Expo (ICME)","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":"2022 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/A5100352041","display_name":"Jiajun Li","orcid":"https://orcid.org/0000-0002-7208-9345"},"institutions":[{"id":"https://openalex.org/I16365422","display_name":"Hefei University of Technology","ror":"https://ror.org/02czkny70","country_code":"CN","type":"education","lineage":["https://openalex.org/I16365422"]},{"id":"https://openalex.org/I4210104624","display_name":"Manufacturing Institute","ror":"https://ror.org/027wewa02","country_code":"US","type":"nonprofit","lineage":["https://openalex.org/I4210104624"]},{"id":"https://openalex.org/I4210165264","display_name":"Guangxi Academy of Sciences","ror":"https://ror.org/054x1kd82","country_code":"CN","type":"government","lineage":["https://openalex.org/I4210165264"]}],"countries":["CN","US"],"is_corresponding":false,"raw_author_name":"Jiajun Li","raw_affiliation_strings":["School of Computer Science and Information Engineering, Hefei University of Technology,Hefei,China,230009","Guangxi Academy of Sciences","Intelligent Interconnected Systems Laboratory of Anhui Province, Hefei University of Technology","Intelligent Manufacturing Institute of HFUT"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Information Engineering, Hefei University of Technology,Hefei,China,230009","institution_ids":["https://openalex.org/I16365422"]},{"raw_affiliation_string":"Guangxi Academy of Sciences","institution_ids":["https://openalex.org/I4210165264"]},{"raw_affiliation_string":"Intelligent Interconnected Systems Laboratory of Anhui Province, Hefei University of Technology","institution_ids":["https://openalex.org/I16365422"]},{"raw_affiliation_string":"Intelligent Manufacturing Institute of HFUT","institution_ids":["https://openalex.org/I4210104624"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100722639","display_name":"Zhong\u2010Qiu Zhao","orcid":"https://orcid.org/0000-0002-0477-4412"},"institutions":[{"id":"https://openalex.org/I16365422","display_name":"Hefei University of Technology","ror":"https://ror.org/02czkny70","country_code":"CN","type":"education","lineage":["https://openalex.org/I16365422"]},{"id":"https://openalex.org/I4210104624","display_name":"Manufacturing Institute","ror":"https://ror.org/027wewa02","country_code":"US","type":"nonprofit","lineage":["https://openalex.org/I4210104624"]},{"id":"https://openalex.org/I4210165264","display_name":"Guangxi Academy of Sciences","ror":"https://ror.org/054x1kd82","country_code":"CN","type":"government","lineage":["https://openalex.org/I4210165264"]}],"countries":["CN","US"],"is_corresponding":false,"raw_author_name":"Zhong-Qiu Zhao","raw_affiliation_strings":["School of Computer Science and Information Engineering, Hefei University of Technology,Hefei,China,230009","Guangxi Academy of Sciences","Intelligent Manufacturing Institute of HFUT","Intelligent Interconnected Systems Laboratory of Anhui Province, Hefei University of Technology"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Information Engineering, Hefei University of Technology,Hefei,China,230009","institution_ids":["https://openalex.org/I16365422"]},{"raw_affiliation_string":"Guangxi Academy of Sciences","institution_ids":["https://openalex.org/I4210165264"]},{"raw_affiliation_string":"Intelligent Manufacturing Institute of HFUT","institution_ids":["https://openalex.org/I4210104624"]},{"raw_affiliation_string":"Intelligent Interconnected Systems Laboratory of Anhui Province, Hefei University of Technology","institution_ids":["https://openalex.org/I16365422"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":3,"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/T11105","display_name":"Advanced Image Processing Techniques","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/T11105","display_name":"Advanced Image Processing Techniques","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/T13114","display_name":"Image Processing Techniques and Applications","score":0.9968000054359436,"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"}},{"id":"https://openalex.org/T10531","display_name":"Advanced Vision and Imaging","score":0.9941999912261963,"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/upsampling","display_name":"Upsampling","score":0.8902981281280518},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.836744487285614},{"id":"https://openalex.org/keywords/adaptive-sampling","display_name":"Adaptive sampling","score":0.7266486287117004},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.624751091003418},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.6246609687805176},{"id":"https://openalex.org/keywords/sorting","display_name":"Sorting","score":0.6026483774185181},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4721153974533081},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.44992318749427795},{"id":"https://openalex.org/keywords/resolution","display_name":"Resolution (logic)","score":0.42426058650016785},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3964197635650635},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.2493819296360016},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.16604557633399963},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.15261974930763245}],"concepts":[{"id":"https://openalex.org/C110384440","wikidata":"https://www.wikidata.org/wiki/Q1143270","display_name":"Upsampling","level":3,"score":0.8902981281280518},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.836744487285614},{"id":"https://openalex.org/C2781395549","wikidata":"https://www.wikidata.org/wiki/Q4680762","display_name":"Adaptive sampling","level":3,"score":0.7266486287117004},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.624751091003418},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.6246609687805176},{"id":"https://openalex.org/C111696304","wikidata":"https://www.wikidata.org/wiki/Q2303697","display_name":"Sorting","level":2,"score":0.6026483774185181},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4721153974533081},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.44992318749427795},{"id":"https://openalex.org/C138268822","wikidata":"https://www.wikidata.org/wiki/Q1051925","display_name":"Resolution (logic)","level":2,"score":0.42426058650016785},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3964197635650635},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2493819296360016},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.16604557633399963},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.15261974930763245},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","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},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"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/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icme52920.2022.9859923","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icme52920.2022.9859923","pdf_url":null,"source":{"id":"https://openalex.org/S4363607799","display_name":"2022 IEEE International Conference on Multimedia and Expo (ICME)","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":"2022 IEEE International Conference on Multimedia and Expo (ICME)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1992687723","display_name":null,"funder_award_id":"61976079","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":38,"referenced_works":["https://openalex.org/W1791560514","https://openalex.org/W1930824406","https://openalex.org/W2047920195","https://openalex.org/W2059432853","https://openalex.org/W2121927366","https://openalex.org/W2132984949","https://openalex.org/W2133665775","https://openalex.org/W2256388387","https://openalex.org/W2296073425","https://openalex.org/W2503339013","https://openalex.org/W2689134854","https://openalex.org/W2739757502","https://openalex.org/W2866634454","https://openalex.org/W2951954464","https://openalex.org/W2963350250","https://openalex.org/W2963351448","https://openalex.org/W2963516811","https://openalex.org/W2963645458","https://openalex.org/W2976718572","https://openalex.org/W2979329071","https://openalex.org/W2984957176","https://openalex.org/W2997813960","https://openalex.org/W2999276659","https://openalex.org/W3034215213","https://openalex.org/W3090680743","https://openalex.org/W3133953507","https://openalex.org/W3171831078","https://openalex.org/W3176997885","https://openalex.org/W4243520228","https://openalex.org/W4294635920","https://openalex.org/W6638194035","https://openalex.org/W6679390333","https://openalex.org/W6724673846","https://openalex.org/W6749061506","https://openalex.org/W6749892895","https://openalex.org/W6753074096","https://openalex.org/W6760278398","https://openalex.org/W6783945198"],"related_works":["https://openalex.org/W17155033","https://openalex.org/W2062399876","https://openalex.org/W3207760230","https://openalex.org/W1496222301","https://openalex.org/W4312814274","https://openalex.org/W1590307681","https://openalex.org/W2536018345","https://openalex.org/W4285370786","https://openalex.org/W2296488620","https://openalex.org/W4293519343"],"abstract_inverted_index":{"The":[0,73],"performance":[1,143],"of":[2,14,23,66,94,144],"super-resolution":[3],"(SR)":[4],"networks":[5],"is":[6],"highly":[7],"dependent":[8],"on":[9,20],"the":[10,15,24,48,63,67,91,113,120,133,142],"quality":[11],"and":[12,56,70,84],"size":[13],"training":[16,114,125],"data.":[17],"However,":[18],"research":[19],"better":[21],"use":[22],"available":[25],"SR":[26,145],"dataset":[27],"remains":[28],"unexplored.":[29],"In":[30],"this":[31,42],"work,":[32],"we":[33],"propose":[34],"Difficulty-based":[35],"Adaptive":[36],"Sampling":[37],"(DAS)":[38],"strategy":[39],"to":[40,45,61,89,102,111],"fill":[41],"gap.":[43],"Specifically,":[44],"further":[46],"exploit":[47],"input":[49,68],"samples,":[50],"DAS":[51,77,116,131],"first":[52],"uses":[53,98],"a":[54,86,99,108],"Calculating":[55],"Sorting":[57],"module":[58,75,101,110],"(CS":[59],"module)":[60],"calculate":[62,90],"upsampling":[64],"difficulty":[65,92],"samples":[69,121,135],"sorts":[71],"them.":[72],"CS":[74],"makes":[76],"be":[78],"efficient":[79],"with":[80,107],"an":[81],"online":[82],"form":[83],"using":[85],"fast":[87],"method":[88],"degrees":[93],"samples.":[95,105],"Then":[96],"it":[97],"Sampler":[100],"select":[103,119],"appropriate":[104,134],"Finally,":[106],"Recorder":[109],"record":[112],"states,":[115],"can":[117,139],"dynamically":[118],"suitable":[122],"for":[123,136],"different":[124],"stages.":[126],"Extensive":[127],"experiments":[128],"demonstrate":[129],"that":[130],"selecting":[132],"each":[137],"iteration":[138],"effectively":[140],"improve":[141],"networks.":[146]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":2}],"updated_date":"2026-07-25T09:21:30.201066","created_date":"2025-10-10T00:00:00"}
