{"id":"https://openalex.org/W3109016549","doi":"https://doi.org/10.1109/tip.2020.3043093","title":"Learning Spatial Attention for Face Super-Resolution","display_name":"Learning Spatial Attention for Face Super-Resolution","publication_year":2020,"publication_date":"2020-12-14","ids":{"openalex":"https://openalex.org/W3109016549","doi":"https://doi.org/10.1109/tip.2020.3043093","mag":"3109016549","pmid":"https://pubmed.ncbi.nlm.nih.gov/33315560"},"language":"en","primary_location":{"id":"doi:10.1109/tip.2020.3043093","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tip.2020.3043093","pdf_url":null,"source":{"id":"https://openalex.org/S4210173141","display_name":"IEEE Transactions on Image Processing","issn_l":"1057-7149","issn":["1057-7149","1941-0042"],"is_oa":false,"is_in_doaj":false,"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":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Image Processing","raw_type":"journal-article"},"type":"article","indexed_in":["arxiv","crossref","pubmed"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2012.01211","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Chaofeng Chen","orcid":"https://orcid.org/0000-0001-6137-5162"},"institutions":[{"id":"https://openalex.org/I889458895","display_name":"University of Hong Kong","ror":"https://ror.org/02zhqgq86","country_code":"HK","type":"education","lineage":["https://openalex.org/I889458895"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Chaofeng Chen","raw_affiliation_strings":["Department of Computer Science, The University of Hong Kong, Hong Kong"],"raw_orcid":"https://orcid.org/0000-0001-6137-5162","affiliations":[{"raw_affiliation_string":"Department of Computer Science, The University of Hong Kong, Hong Kong","institution_ids":["https://openalex.org/I889458895"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Dihong Gong","orcid":null},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dihong Gong","raw_affiliation_strings":["Tencent AI Lab, Shenzhen, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tencent AI Lab, Shenzhen, China","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Hao Wang","orcid":"https://orcid.org/0000-0002-3540-2371"},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hao Wang","raw_affiliation_strings":["Tencent AI Lab, Shenzhen, China"],"raw_orcid":"https://orcid.org/0000-0002-3540-2371","affiliations":[{"raw_affiliation_string":"Tencent AI Lab, Shenzhen, China","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Zhifeng Li","orcid":null},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhifeng Li","raw_affiliation_strings":["Tencent AI Lab, Shenzhen, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tencent AI Lab, Shenzhen, China","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"last","author":{"id":null,"display_name":"Kwan-Yee K. Wong","orcid":"https://orcid.org/0000-0001-8560-9007"},"institutions":[{"id":"https://openalex.org/I889458895","display_name":"University of Hong Kong","ror":"https://ror.org/02zhqgq86","country_code":"HK","type":"education","lineage":["https://openalex.org/I889458895"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Kwan-Yee K. Wong","raw_affiliation_strings":["Department of Computer Science, The University of Hong Kong, Hong Kong"],"raw_orcid":"https://orcid.org/0000-0001-8560-9007","affiliations":[{"raw_affiliation_string":"Department of Computer Science, The University of Hong Kong, Hong Kong","institution_ids":["https://openalex.org/I889458895"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":9.6245,"has_fulltext":false,"cited_by_count":213,"citation_normalized_percentile":{"value":0.98833001,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":"30","issue":null,"first_page":"1219","last_page":"1231"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11105","display_name":"Advanced Image Processing Techniques","score":0.4936999976634979,"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":0.4936999976634979,"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/T11448","display_name":"Face recognition and analysis","score":0.3328000009059906,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.09600000083446503,"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/landmark","display_name":"Landmark","score":0.7889000177383423},{"id":"https://openalex.org/keywords/face","display_name":"Face (sociological concept)","score":0.6740000247955322},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6389999985694885},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.517799973487854},{"id":"https://openalex.org/keywords/image-resolution","display_name":"Image resolution","score":0.4934000074863434},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.47589999437332153},{"id":"https://openalex.org/keywords/visualization","display_name":"Visualization","score":0.4498000144958496},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.4472000002861023},{"id":"https://openalex.org/keywords/facial-recognition-system","display_name":"Facial recognition system","score":0.44440001249313354}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8493000268936157},{"id":"https://openalex.org/C2780297707","wikidata":"https://www.wikidata.org/wiki/Q4895393","display_name":"Landmark","level":2,"score":0.7889000177383423},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7821000218391418},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.6740000247955322},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6389999985694885},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5382000207901001},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.517799973487854},{"id":"https://openalex.org/C205372480","wikidata":"https://www.wikidata.org/wiki/Q210521","display_name":"Image resolution","level":2,"score":0.4934000074863434},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.47589999437332153},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.4498000144958496},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.4472000002861023},{"id":"https://openalex.org/C31510193","wikidata":"https://www.wikidata.org/wiki/Q1192553","display_name":"Facial recognition system","level":3,"score":0.44440001249313354},{"id":"https://openalex.org/C186644900","wikidata":"https://www.wikidata.org/wiki/Q194152","display_name":"Parsing","level":2,"score":0.41429999470710754},{"id":"https://openalex.org/C4641261","wikidata":"https://www.wikidata.org/wiki/Q11681085","display_name":"Face detection","level":4,"score":0.3846000134944916},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.37790000438690186},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.36629998683929443},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3612000048160553},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.3091999888420105},{"id":"https://openalex.org/C28006648","wikidata":"https://www.wikidata.org/wiki/Q6934509","display_name":"Multi-task learning","level":3,"score":0.28850001096725464},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.28110000491142273},{"id":"https://openalex.org/C138268822","wikidata":"https://www.wikidata.org/wiki/Q1051925","display_name":"Resolution (logic)","level":2,"score":0.2782000005245209},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.2709999978542328},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.267300009727478},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.2667999863624573},{"id":"https://openalex.org/C159620131","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Spatial analysis","level":2,"score":0.25859999656677246}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/tip.2020.3043093","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tip.2020.3043093","pdf_url":null,"source":{"id":"https://openalex.org/S4210173141","display_name":"IEEE Transactions on Image Processing","issn_l":"1057-7149","issn":["1057-7149","1941-0042"],"is_oa":false,"is_in_doaj":false,"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":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Image Processing","raw_type":"journal-article"},{"id":"pmid:33315560","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/33315560","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE transactions on image processing : a publication of the IEEE Signal Processing Society","raw_type":null},{"id":"pmh:oai:arXiv.org:2012.01211","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2012.01211","pdf_url":"https://arxiv.org/pdf/2012.01211","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":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2012.01211","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2012.01211","pdf_url":"https://arxiv.org/pdf/2012.01211","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":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":65,"referenced_works":["https://openalex.org/W1677182931","https://openalex.org/W1834627138","https://openalex.org/W1972002222","https://openalex.org/W1972495287","https://openalex.org/W2003749430","https://openalex.org/W2006902452","https://openalex.org/W2055603434","https://openalex.org/W2097039786","https://openalex.org/W2104462292","https://openalex.org/W2140257560","https://openalex.org/W2141631520","https://openalex.org/W2152131136","https://openalex.org/W2165421528","https://openalex.org/W2300234500","https://openalex.org/W2307770531","https://openalex.org/W2341528187","https://openalex.org/W2503339013","https://openalex.org/W2507235960","https://openalex.org/W2520930090","https://openalex.org/W2535388113","https://openalex.org/W2550553598","https://openalex.org/W2732016772","https://openalex.org/W2740535357","https://openalex.org/W2741976748","https://openalex.org/W2752782242","https://openalex.org/W2768814045","https://openalex.org/W2776107444","https://openalex.org/W2780624730","https://openalex.org/W2798691622","https://openalex.org/W2884585870","https://openalex.org/W2895542678","https://openalex.org/W2949662773","https://openalex.org/W2962770929","https://openalex.org/W2962785568","https://openalex.org/W2962974533","https://openalex.org/W2963089432","https://openalex.org/W2963372104","https://openalex.org/W2963393566","https://openalex.org/W2963466847","https://openalex.org/W2963470893","https://openalex.org/W2963495494","https://openalex.org/W2963516811","https://openalex.org/W2963583792","https://openalex.org/W2963676087","https://openalex.org/W2963800363","https://openalex.org/W2963814095","https://openalex.org/W2964167901","https://openalex.org/W2969834519","https://openalex.org/W3035605421","https://openalex.org/W6630875275","https://openalex.org/W6631190155","https://openalex.org/W6637373629","https://openalex.org/W6675479722","https://openalex.org/W6682137061","https://openalex.org/W6684221638","https://openalex.org/W6684783560","https://openalex.org/W6698183232","https://openalex.org/W6729874835","https://openalex.org/W6735593745","https://openalex.org/W6743792510","https://openalex.org/W6745560452","https://openalex.org/W6753074096","https://openalex.org/W6754405603","https://openalex.org/W6765779288","https://openalex.org/W6771275388"],"related_works":[],"abstract_inverted_index":{"General":[0],"image":[1],"super-resolution":[2],"techniques":[3],"have":[4,27],"difficulties":[5],"in":[6],"recovering":[7],"detailed":[8],"face":[9,16,25,39,64,97,124,146,157,174,249,262],"structures":[10,125,147,175],"when":[11],"applying":[12],"to":[13,106,116,121,130,220,257],"low":[14,62,180,260],"resolution":[15,63,181,223,244],"images.":[17,263],"Recent":[18],"deep":[19],"learning":[20,46],"based":[21],"methods":[22],"tailored":[23],"for":[24,96,150,178,246],"images":[26,65],"achieved":[28],"improved":[29],"performance":[30],"by":[31],"jointly":[32],"trained":[33,232],"with":[34,214,233],"additional":[35],"task":[36],"such":[37],"as":[38,143,218],"parsing":[40],"and":[41,69,126,141,198,242],"landmark":[42,199],"prediction.":[43],"However,":[44],"multi-task":[45],"requires":[47],"extra":[48],"manually":[49],"labeled":[50],"data.":[51],"Besides,":[52],"most":[53],"of":[54,155,160,191,204],"the":[55,107,113,122,137,144,156,161,172,202],"existing":[56],"works":[57],"can":[58,170,236],"only":[59,148,238],"generate":[60],"relatively":[61],"(e.g.,":[66,183],"128\u00d7128":[67],"),":[68],"their":[70],"applications":[71],"are":[72,265],"therefore":[73],"limited.":[74],"In":[75],"this":[76],"paper,":[77],"we":[78,100],"introduce":[79,101],"a":[80,102,151],"novel":[81],"SPatial":[82],"Attention":[83,93],"Residual":[84],"Network":[85],"(SPARNet)":[86],"built":[87],"on":[88,188],"our":[89,166,205],"newly":[90],"proposed":[91],"Face":[92],"Units":[94],"(FAUs)":[95],"super-resolution.":[98],"Specifically,":[99],"spatial":[103,167],"attention":[104,129,162,168],"mechanism":[105],"vanilla":[108],"residual":[109],"blocks.":[110],"This":[111,135],"enables":[112],"convolutional":[114],"layers":[115],"adaptively":[117],"bootstrap":[118],"features":[119],"related":[120],"key":[123,145,173],"pay":[127],"less":[128,132],"those":[131],"feature-rich":[133],"regions.":[134],"makes":[136],"training":[138],"more":[139],"effective":[140],"efficient":[142],"account":[149],"very":[152,179],"small":[153],"portion":[154],"image.":[158],"Visualization":[159],"maps":[163],"shows":[164],"that":[165,230],"network":[169],"capture":[171],"well":[176],"even":[177],"faces":[182],"16\u00d716":[184],").":[185,227],"Quantitative":[186],"comparisons":[187],"various":[189],"kinds":[190],"metrics":[192],"(including":[193],"PSNR,":[194],"SSIM,":[195],"identity":[196],"similarity,":[197],"detection)":[200],"demonstrate":[201],"superiority":[203],"method":[206],"over":[207],"current":[208],"state-of-the-arts.":[209],"We":[210,228],"further":[211],"extend":[212],"SPARNet":[213],"multi-scale":[215],"discriminators,":[216],"named":[217],"SPARNetHD,":[219],"produce":[221,239],"high":[222,240,243],"results":[224],"(i.e.,":[225],"512\u00d7512":[226],"show":[229,253],"SPARNetHD":[231],"synthetic":[234],"data":[235],"not":[237],"quality":[241,261],"outputs":[245],"synthetically":[247],"degraded":[248],"images,":[250],"but":[251],"also":[252],"good":[254],"generalization":[255],"ability":[256],"real":[258],"world":[259],"Codes":[264],"available":[266],"at":[267],"https://github.com/chaofengc/Face-SPARNet.":[268]},"counts_by_year":[{"year":2026,"cited_by_count":20},{"year":2025,"cited_by_count":41},{"year":2024,"cited_by_count":52},{"year":2023,"cited_by_count":49},{"year":2022,"cited_by_count":42},{"year":2021,"cited_by_count":9}],"updated_date":"2026-07-31T08:31:51.225901","created_date":"2020-12-07T00:00:00"}
