{"id":"https://openalex.org/W3161098201","doi":"https://doi.org/10.1109/icassp39728.2021.9414892","title":"HOCA: Higher-Order Channel Attention for Single Image Super-Resolution","display_name":"HOCA: Higher-Order Channel Attention for Single Image Super-Resolution","publication_year":2021,"publication_date":"2021-05-13","ids":{"openalex":"https://openalex.org/W3161098201","doi":"https://doi.org/10.1109/icassp39728.2021.9414892","mag":"3161098201"},"language":"en","primary_location":{"id":"doi:10.1109/icassp39728.2021.9414892","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp39728.2021.9414892","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","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/A5112639583","display_name":"Yalei Lv","orcid":null},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]},{"id":"https://openalex.org/I4210114105","display_name":"Tsinghua\u2013Berkeley Shenzhen Institute","ror":"https://ror.org/02hhwwz98","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210114105","https://openalex.org/I95457486","https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yalei Lv","raw_affiliation_strings":["Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China","institution_ids":["https://openalex.org/I4210114105","https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5023762528","display_name":"Tao Dai","orcid":"https://orcid.org/0000-0003-0594-6404"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]},{"id":"https://openalex.org/I4210114105","display_name":"Tsinghua\u2013Berkeley Shenzhen Institute","ror":"https://ror.org/02hhwwz98","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210114105","https://openalex.org/I95457486","https://openalex.org/I99065089"]},{"id":"https://openalex.org/I4210136793","display_name":"Peng Cheng Laboratory","ror":"https://ror.org/03qdqbt06","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210136793"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tao Dai","raw_affiliation_strings":["PCL Research Center of Networks and Communications, Peng Cheng Laboratory, Shenzhen, China","Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"PCL Research Center of Networks and Communications, Peng Cheng Laboratory, Shenzhen, China","institution_ids":["https://openalex.org/I4210136793"]},{"raw_affiliation_string":"Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China","institution_ids":["https://openalex.org/I4210114105","https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100427338","display_name":"Bin Chen","orcid":"https://orcid.org/0000-0002-4798-230X"},"institutions":[{"id":"https://openalex.org/I4210114105","display_name":"Tsinghua\u2013Berkeley Shenzhen Institute","ror":"https://ror.org/02hhwwz98","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210114105","https://openalex.org/I95457486","https://openalex.org/I99065089"]},{"id":"https://openalex.org/I4210136793","display_name":"Peng Cheng Laboratory","ror":"https://ror.org/03qdqbt06","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210136793"]},{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bin Chen","raw_affiliation_strings":["PCL Research Center of Networks and Communications, Peng Cheng Laboratory, Shenzhen, China","Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"PCL Research Center of Networks and Communications, Peng Cheng Laboratory, Shenzhen, China","institution_ids":["https://openalex.org/I4210136793"]},{"raw_affiliation_string":"Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China","institution_ids":["https://openalex.org/I4210114105","https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5066196985","display_name":"Jian L\u00fc","orcid":"https://orcid.org/0000-0003-4599-7281"},"institutions":[{"id":"https://openalex.org/I180726961","display_name":"Shenzhen University","ror":"https://ror.org/01vy4gh70","country_code":"CN","type":"education","lineage":["https://openalex.org/I180726961"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jian Lu","raw_affiliation_strings":["Shenzhen Key Laboratory of Advanced Machine Learning and Applications, Shenzhen University, China"],"affiliations":[{"raw_affiliation_string":"Shenzhen Key Laboratory of Advanced Machine Learning and Applications, Shenzhen University, China","institution_ids":["https://openalex.org/I180726961"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5034104790","display_name":"Shu\u2010Tao Xia","orcid":"https://orcid.org/0000-0002-8639-982X"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]},{"id":"https://openalex.org/I4210114105","display_name":"Tsinghua\u2013Berkeley Shenzhen Institute","ror":"https://ror.org/02hhwwz98","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210114105","https://openalex.org/I95457486","https://openalex.org/I99065089"]},{"id":"https://openalex.org/I4210136793","display_name":"Peng Cheng Laboratory","ror":"https://ror.org/03qdqbt06","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210136793"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shu-Tao Xia","raw_affiliation_strings":["PCL Research Center of Networks and Communications, Peng Cheng Laboratory, Shenzhen, China","Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"PCL Research Center of Networks and Communications, Peng Cheng Laboratory, Shenzhen, China","institution_ids":["https://openalex.org/I4210136793"]},{"raw_affiliation_string":"Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China","institution_ids":["https://openalex.org/I4210114105","https://openalex.org/I99065089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5082088474","display_name":"Jingchao Cao","orcid":"https://orcid.org/0000-0002-8944-8033"},"institutions":[{"id":"https://openalex.org/I168719708","display_name":"City University of Hong Kong","ror":"https://ror.org/03q8dnn23","country_code":"HK","type":"education","lineage":["https://openalex.org/I168719708"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Jingchao Cao","raw_affiliation_strings":["City University of Hong Kong, Hong Kong"],"affiliations":[{"raw_affiliation_string":"City University of Hong Kong, Hong Kong","institution_ids":["https://openalex.org/I168719708"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5112639583"],"corresponding_institution_ids":["https://openalex.org/I4210114105","https://openalex.org/I99065089"],"apc_list":null,"apc_paid":null,"fwci":0.3843,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.59403595,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1605","last_page":"1609"},"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":0.9972000122070312,"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":0.9940000176429749,"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/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7432937622070312},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7368277907371521},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.6861708760261536},{"id":"https://openalex.org/keywords/channel","display_name":"Channel (broadcasting)","score":0.6397480964660645},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.607490599155426},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.5321927070617676},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4900158941745758},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4620015323162079},{"id":"https://openalex.org/keywords/order","display_name":"Order (exchange)","score":0.44167277216911316},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.41878652572631836},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.3231548070907593},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.321071982383728},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.07245996594429016}],"concepts":[{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7432937622070312},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7368277907371521},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6861708760261536},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.6397480964660645},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.607490599155426},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.5321927070617676},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4900158941745758},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4620015323162079},{"id":"https://openalex.org/C182306322","wikidata":"https://www.wikidata.org/wiki/Q1779371","display_name":"Order (exchange)","level":2,"score":0.44167277216911316},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.41878652572631836},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3231548070907593},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.321071982383728},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.07245996594429016},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C10138342","wikidata":"https://www.wikidata.org/wiki/Q43015","display_name":"Finance","level":1,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"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/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp39728.2021.9414892","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp39728.2021.9414892","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320321543","display_name":"China Postdoctoral Science Foundation","ror":"https://ror.org/0426zh255"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":34,"referenced_works":["https://openalex.org/W10548402","https://openalex.org/W1522301498","https://openalex.org/W1665214252","https://openalex.org/W1791560514","https://openalex.org/W1885185971","https://openalex.org/W1930824406","https://openalex.org/W2121927366","https://openalex.org/W2133665775","https://openalex.org/W2192954843","https://openalex.org/W2242218935","https://openalex.org/W2476548250","https://openalex.org/W2547875792","https://openalex.org/W2601789736","https://openalex.org/W2739757502","https://openalex.org/W2866634454","https://openalex.org/W2884751099","https://openalex.org/W2899771611","https://openalex.org/W2954930822","https://openalex.org/W2963091558","https://openalex.org/W2963372104","https://openalex.org/W2963420686","https://openalex.org/W2963495494","https://openalex.org/W2963550079","https://openalex.org/W2964101377","https://openalex.org/W2964121744","https://openalex.org/W3101659800","https://openalex.org/W4377561911","https://openalex.org/W6631190155","https://openalex.org/W6637242042","https://openalex.org/W6638194035","https://openalex.org/W6690498163","https://openalex.org/W6729448088","https://openalex.org/W6743731764","https://openalex.org/W6753179690"],"related_works":["https://openalex.org/W4293226380","https://openalex.org/W4321487865","https://openalex.org/W4313906399","https://openalex.org/W2811106690","https://openalex.org/W4239306820","https://openalex.org/W2947043951","https://openalex.org/W2318112981","https://openalex.org/W4312417841","https://openalex.org/W2062195135","https://openalex.org/W4210874298"],"abstract_inverted_index":{"Convolutional":[0],"neural":[1],"networks":[2],"(CNNs)":[3],"have":[4,20],"obtained":[5,21],"great":[6],"success":[7],"in":[8],"single":[9],"image":[10],"super-resolution":[11],"(SR).":[12],"More":[13],"recent":[14],"works":[15],"(e.g.,":[16],"RCAN":[17],"and":[18,127],"SAN)":[19],"remarkable":[22],"performance":[23,151],"with":[24],"channel":[25,61,92,116],"attention":[26,62,93],"based":[27],"on":[28,140],"first-":[29],"or":[30],"second-order":[31],"statistics":[32,42],"of":[33,51,70,80,87,102,152],"features.":[34],"However,":[35],"these":[36],"methods":[37],"neglect":[38],"the":[39,48,67,96,100,150],"rich":[40],"feature":[41,88,97],"higher":[43],"than":[44],"second-order,":[45],"thus":[46],"hindering":[47],"representation":[49,68],"ability":[50,69],"CNNs.":[52,71],"To":[53],"address":[54],"this":[55],"issue,":[56],"we":[57,83,105],"propose":[58],"a":[59,107,113,124],"higher-order":[60],"(HOCA)":[63],"module":[64,121,126,147],"to":[65,76,94,110],"enhance":[66],"In":[72],"our":[73,119,145],"HOCA":[74,120,146],"module,":[75],"capture":[77],"different":[78],"types":[79],"semantic":[81],"information,":[82],"first":[84],"compute":[85],"k-order":[86,115],"statistics,":[89],"followed":[90],"by":[91],"learn":[95],"interdependencies.":[98],"Considering":[99],"diversity":[101],"input":[103],"contents,":[104],"design":[106],"gate":[108],"mechanism":[109],"adaptively":[111],"select":[112],"specific":[114],"attention.":[117],"Besides,":[118],"serves":[122],"as":[123],"plug-and-play":[125],"can":[128],"be":[129],"easily":[130],"plugged":[131],"into":[132],"existing":[133],"state-of-art":[134],"CNN-based":[135,154],"SR":[136,155],"methods.":[137,156],"Extensive":[138],"experiments":[139],"public":[141],"benchmarks":[142],"show":[143],"that":[144],"effectively":[148],"improves":[149],"various":[153]},"counts_by_year":[{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}