{"id":"https://openalex.org/W3106970856","doi":"https://doi.org/10.1109/lsp.2020.3039952","title":"Lightweight Deep CNN for Natural Image Matting via Similarity-Preserving Knowledge Distillation","display_name":"Lightweight Deep CNN for Natural Image Matting via Similarity-Preserving Knowledge Distillation","publication_year":2020,"publication_date":"2020-01-01","ids":{"openalex":"https://openalex.org/W3106970856","doi":"https://doi.org/10.1109/lsp.2020.3039952","mag":"3106970856"},"language":"en","primary_location":{"id":"doi:10.1109/lsp.2020.3039952","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lsp.2020.3039952","pdf_url":null,"source":{"id":"https://openalex.org/S120629676","display_name":"IEEE Signal Processing Letters","issn_l":"1070-9908","issn":["1070-9908","1558-2361"],"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 Signal Processing Letters","raw_type":"journal-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/A5018799210","display_name":"Donggeun Yoon","orcid":"https://orcid.org/0000-0001-6153-8056"},"institutions":[{"id":"https://openalex.org/I196345858","display_name":"Chungnam National University","ror":"https://ror.org/0227as991","country_code":"KR","type":"education","lineage":["https://openalex.org/I196345858"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Donggeun Yoon","raw_affiliation_strings":["Department of Electronics Engineering, Chungnam National University Daedeok Campus, Daejeon, South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electronics Engineering, Chungnam National University Daedeok Campus, Daejeon, South Korea","institution_ids":["https://openalex.org/I196345858"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079380164","display_name":"Jinsun Park","orcid":"https://orcid.org/0000-0002-2296-819X"},"institutions":[{"id":"https://openalex.org/I157485424","display_name":"Korea Advanced Institute of Science and Technology","ror":"https://ror.org/05apxxy63","country_code":"KR","type":"education","lineage":["https://openalex.org/I157485424"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Jinsun Park","raw_affiliation_strings":["School of Electrical Engineering, KAIST, Daejeon, South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electrical Engineering, KAIST, Daejeon, South Korea","institution_ids":["https://openalex.org/I157485424"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5016424347","display_name":"Donghyeon Cho","orcid":"https://orcid.org/0000-0002-2184-921X"},"institutions":[{"id":"https://openalex.org/I196345858","display_name":"Chungnam National University","ror":"https://ror.org/0227as991","country_code":"KR","type":"education","lineage":["https://openalex.org/I196345858"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Donghyeon Cho","raw_affiliation_strings":["Department of Electronics Engineering, Chungnam National University Daedeok Campus, Daejeon, South Korea"],"raw_orcid":"https://orcid.org/0000-0002-2184-921X","affiliations":[{"raw_affiliation_string":"Department of Electronics Engineering, Chungnam National University Daedeok Campus, Daejeon, South Korea","institution_ids":["https://openalex.org/I196345858"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.1083,"has_fulltext":false,"cited_by_count":36,"citation_normalized_percentile":{"value":0.8956403,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":"27","issue":null,"first_page":"2139","last_page":"2143"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11019","display_name":"Image Enhancement 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/T11019","display_name":"Image Enhancement 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/T10688","display_name":"Image and Signal Denoising Methods","score":0.9937999844551086,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9914000034332275,"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/computer-science","display_name":"Computer science","score":0.7477617263793945},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.7143134474754333},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7101242542266846},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6897182464599609},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6846567988395691},{"id":"https://openalex.org/keywords/pairwise-comparison","display_name":"Pairwise comparison","score":0.6504355669021606},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6417971253395081},{"id":"https://openalex.org/keywords/distillation","display_name":"Distillation","score":0.581726610660553},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.4939900040626526},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.44413483142852783},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4355352520942688}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7477617263793945},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.7143134474754333},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7101242542266846},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6897182464599609},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6846567988395691},{"id":"https://openalex.org/C184898388","wikidata":"https://www.wikidata.org/wiki/Q1435712","display_name":"Pairwise comparison","level":2,"score":0.6504355669021606},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6417971253395081},{"id":"https://openalex.org/C204030448","wikidata":"https://www.wikidata.org/wiki/Q101017","display_name":"Distillation","level":2,"score":0.581726610660553},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4939900040626526},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.44413483142852783},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4355352520942688},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C178790620","wikidata":"https://www.wikidata.org/wiki/Q11351","display_name":"Organic chemistry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/lsp.2020.3039952","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lsp.2020.3039952","pdf_url":null,"source":{"id":"https://openalex.org/S120629676","display_name":"IEEE Signal Processing Letters","issn_l":"1070-9908","issn":["1070-9908","1558-2361"],"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 Signal Processing Letters","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320321204","display_name":"Chungnam National University","ror":"https://ror.org/0227as991"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":49,"referenced_works":["https://openalex.org/W106417640","https://openalex.org/W1821462560","https://openalex.org/W2007640671","https://openalex.org/W2035773017","https://openalex.org/W2044673646","https://openalex.org/W2077604257","https://openalex.org/W2080240714","https://openalex.org/W2098554565","https://openalex.org/W2103917701","https://openalex.org/W2109815552","https://openalex.org/W2114378523","https://openalex.org/W2142771691","https://openalex.org/W2169040970","https://openalex.org/W2254478436","https://openalex.org/W2293703294","https://openalex.org/W2295475768","https://openalex.org/W2296349909","https://openalex.org/W2296558186","https://openalex.org/W2513742795","https://openalex.org/W2518810941","https://openalex.org/W2520247582","https://openalex.org/W2541775796","https://openalex.org/W2561238782","https://openalex.org/W2586357953","https://openalex.org/W2604469346","https://openalex.org/W2736851683","https://openalex.org/W2739879705","https://openalex.org/W2752782242","https://openalex.org/W2894012822","https://openalex.org/W2901505625","https://openalex.org/W2948070565","https://openalex.org/W2963304750","https://openalex.org/W2963420686","https://openalex.org/W2963659778","https://openalex.org/W2982157312","https://openalex.org/W2982242214","https://openalex.org/W2984016939","https://openalex.org/W2985469567","https://openalex.org/W2997597456","https://openalex.org/W3034368090","https://openalex.org/W3098772678","https://openalex.org/W3106153666","https://openalex.org/W4289744118","https://openalex.org/W6638523607","https://openalex.org/W6675705777","https://openalex.org/W6726515866","https://openalex.org/W6729097550","https://openalex.org/W6730179637","https://openalex.org/W6752763669"],"related_works":["https://openalex.org/W2378211422","https://openalex.org/W2487162673","https://openalex.org/W2793211469","https://openalex.org/W2949152769","https://openalex.org/W4372354731","https://openalex.org/W2942366970","https://openalex.org/W2807634898","https://openalex.org/W4321353415","https://openalex.org/W2745001401","https://openalex.org/W1692008701"],"abstract_inverted_index":{"Recently,":[0],"alpha":[1,18],"matting":[2,19,42,139],"has":[3],"witnessed":[4],"remarkable":[5],"growth":[6],"by":[7],"wide":[8],"and":[9,77,102],"deep":[10,16,137],"convolutional":[11],"neural":[12],"networks.":[13],"However,":[14],"previous":[15],"learning-based":[17],"methods":[20],"require":[21],"a":[22,38,45,59,67,100],"high":[23],"computational":[24],"cost":[25],"to":[26,64,81,87,116,135],"be":[27,132],"used":[28],"in":[29,155],"real":[30],"environments":[31],"including":[32],"mobile":[33,147],"devices.":[34],"In":[35,126],"this":[36],"letter,":[37],"lightweight":[39],"natural":[40],"image":[41,138],"network":[43,62,105,121],"with":[44],"similarity-preserving":[46,52,93],"knowledge":[47,53,83,94,124],"distillation":[48,54],"is":[49,144,164],"developed.":[50],"The":[51,70,158],"makes":[55],"pairwise":[56,71],"similarities":[57],"from":[58,66],"compact":[60],"student":[61,104,120],"similar":[63],"those":[65],"teacher":[68,86,108],"network.":[69],"similarity":[72],"measured":[73],"on":[74,91,166],"spatial,":[75],"channel,":[76],"batch":[78],"units":[79],"enables":[80],"transfer":[82],"of":[84,118,160],"the":[85,88,92,107,119,123,128,161],"student.":[89],"Based":[90],"distillation,":[95],"we":[96],"not":[97],"only":[98],"design":[99],"lighter":[101],"smaller":[103],"than":[106],"one":[109],"but":[110],"also":[111],"achieve":[112],"superior":[113],"performance":[114],"compared":[115],"that":[117],"without":[122],"distillation.":[125],"addition,":[127],"proposed":[129,162],"algorithm":[130,143,163],"can":[131],"seamlessly":[133],"applied":[134],"various":[136],"algorithms.":[140],"Therefore,":[141],"our":[142],"effective":[145],"for":[146],"applications":[148],"(e.g.,":[149],"human":[150],"portrait":[151],"matting),":[152],"which":[153],"are":[154],"growing":[156],"demand.":[157],"effectiveness":[159],"verified":[165],"two":[167],"public":[168],"benchmark":[169],"datasets.":[170]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":10},{"year":2021,"cited_by_count":5},{"year":2020,"cited_by_count":2}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}