{"id":"https://openalex.org/W3010290049","doi":"https://doi.org/10.1109/wacv45572.2020.9093588","title":"SINet: Extreme Lightweight Portrait Segmentation Networks with Spatial Squeeze Modules and Information Blocking Decoder","display_name":"SINet: Extreme Lightweight Portrait Segmentation Networks with Spatial Squeeze Modules and Information Blocking Decoder","publication_year":2020,"publication_date":"2020-03-01","ids":{"openalex":"https://openalex.org/W3010290049","doi":"https://doi.org/10.1109/wacv45572.2020.9093588","mag":"3010290049"},"language":"en","primary_location":{"id":"doi:10.1109/wacv45572.2020.9093588","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wacv45572.2020.9093588","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE Winter Conference on Applications of Computer Vision (WACV)","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/A5100442175","display_name":"Hyojin Park","orcid":"https://orcid.org/0000-0003-4814-8330"},"institutions":[{"id":"https://openalex.org/I139264467","display_name":"Seoul National University","ror":"https://ror.org/04h9pn542","country_code":"KR","type":"education","lineage":["https://openalex.org/I139264467"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Hyojin Park","raw_affiliation_strings":["Seoul National University"],"affiliations":[{"raw_affiliation_string":"Seoul National University","institution_ids":["https://openalex.org/I139264467"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079655772","display_name":"Lars Lowe Sj\u00f6sund","orcid":null},"institutions":[{"id":"https://openalex.org/I60922564","display_name":"Naver (South Korea)","ror":"https://ror.org/04nzrnx83","country_code":"KR","type":"company","lineage":["https://openalex.org/I60922564"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Lars Lowe Sjosund","raw_affiliation_strings":["Clova AI, NAVER Corp"],"affiliations":[{"raw_affiliation_string":"Clova AI, NAVER Corp","institution_ids":["https://openalex.org/I60922564"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5073731761","display_name":"Youngjoon Yoo","orcid":"https://orcid.org/0000-0001-8084-5422"},"institutions":[{"id":"https://openalex.org/I60922564","display_name":"Naver (South Korea)","ror":"https://ror.org/04nzrnx83","country_code":"KR","type":"company","lineage":["https://openalex.org/I60922564"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"YoungJoon Yoo","raw_affiliation_strings":["Clova AI, NAVER Corp"],"affiliations":[{"raw_affiliation_string":"Clova AI, NAVER Corp","institution_ids":["https://openalex.org/I60922564"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044430295","display_name":"Nicolas Monet","orcid":null},"institutions":[{"id":"https://openalex.org/I60922564","display_name":"Naver (South Korea)","ror":"https://ror.org/04nzrnx83","country_code":"KR","type":"company","lineage":["https://openalex.org/I60922564"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Nicolas Monet","raw_affiliation_strings":["NAVER LABS, Europe"],"affiliations":[{"raw_affiliation_string":"NAVER LABS, Europe","institution_ids":["https://openalex.org/I60922564"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5075968414","display_name":"Jihwan Bang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jihwan Bang","raw_affiliation_strings":["Search Solutions, Inc"],"affiliations":[{"raw_affiliation_string":"Search Solutions, Inc","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5084897975","display_name":"Nojun Kwak","orcid":"https://orcid.org/0000-0002-1792-0327"},"institutions":[{"id":"https://openalex.org/I139264467","display_name":"Seoul National University","ror":"https://ror.org/04h9pn542","country_code":"KR","type":"education","lineage":["https://openalex.org/I139264467"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Nojun Kwak","raw_affiliation_strings":["Seoul National University"],"affiliations":[{"raw_affiliation_string":"Seoul National University","institution_ids":["https://openalex.org/I139264467"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5100442175"],"corresponding_institution_ids":["https://openalex.org/I139264467"],"apc_list":null,"apc_paid":null,"fwci":2.8459,"has_fulltext":false,"cited_by_count":34,"citation_normalized_percentile":{"value":0.92273045,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"2055","last_page":"2063"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11448","display_name":"Face recognition and analysis","score":0.9997000098228455,"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/T11448","display_name":"Face recognition and analysis","score":0.9997000098228455,"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":0.9970999956130981,"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/T11605","display_name":"Visual Attention and Saliency Detection","score":0.989799976348877,"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":0.8287527561187744},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.8282649517059326},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5287408828735352},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.49145400524139404},{"id":"https://openalex.org/keywords/blocking","display_name":"Blocking (statistics)","score":0.4886205792427063},{"id":"https://openalex.org/keywords/scale-space-segmentation","display_name":"Scale-space segmentation","score":0.445832759141922},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.4280615448951721},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.40120819211006165},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3722159266471863}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8287527561187744},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.8282649517059326},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5287408828735352},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.49145400524139404},{"id":"https://openalex.org/C144745244","wikidata":"https://www.wikidata.org/wiki/Q4927286","display_name":"Blocking (statistics)","level":2,"score":0.4886205792427063},{"id":"https://openalex.org/C65885262","wikidata":"https://www.wikidata.org/wiki/Q7429708","display_name":"Scale-space segmentation","level":4,"score":0.445832759141922},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4280615448951721},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.40120819211006165},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3722159266471863},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/wacv45572.2020.9093588","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wacv45572.2020.9093588","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE Winter Conference on Applications of Computer Vision (WACV)","raw_type":"proceedings-article"},{"id":"pmh:oai:s-space.snu.ac.kr:10371/206023","is_oa":false,"landing_page_url":"https://hdl.handle.net/10371/206023","pdf_url":null,"source":{"id":"https://openalex.org/S4306401345","display_name":"Seoul National University Open Repository (Seoul National University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I139264467","host_organization_name":"Seoul National University","host_organization_lineage":["https://openalex.org/I139264467"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11","score":0.8399999737739563}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":40,"referenced_works":["https://openalex.org/W611457968","https://openalex.org/W1745334888","https://openalex.org/W1903029394","https://openalex.org/W2121295188","https://openalex.org/W2400000673","https://openalex.org/W2412782625","https://openalex.org/W2419448466","https://openalex.org/W2549139847","https://openalex.org/W2560023338","https://openalex.org/W2592939477","https://openalex.org/W2630837129","https://openalex.org/W2787091153","https://openalex.org/W2803097213","https://openalex.org/W2804488433","https://openalex.org/W2883780447","https://openalex.org/W2886934227","https://openalex.org/W2921526792","https://openalex.org/W2934985924","https://openalex.org/W2944779197","https://openalex.org/W2949391068","https://openalex.org/W2949786817","https://openalex.org/W2962772649","https://openalex.org/W2962891619","https://openalex.org/W2963091558","https://openalex.org/W2963418739","https://openalex.org/W2963840672","https://openalex.org/W2964309882","https://openalex.org/W2964350391","https://openalex.org/W2965063910","https://openalex.org/W2970957794","https://openalex.org/W2981899103","https://openalex.org/W2989373050","https://openalex.org/W2997225633","https://openalex.org/W4289121656","https://openalex.org/W4293406525","https://openalex.org/W6694260854","https://openalex.org/W6748481559","https://openalex.org/W6749046737","https://openalex.org/W6753767121","https://openalex.org/W6754123467"],"related_works":["https://openalex.org/W1986655823","https://openalex.org/W2185902295","https://openalex.org/W2103507220","https://openalex.org/W3144569342","https://openalex.org/W3011384228","https://openalex.org/W2124969951","https://openalex.org/W2945274617","https://openalex.org/W4313052709","https://openalex.org/W4298131179","https://openalex.org/W2375430703"],"abstract_inverted_index":{"Designing":[0],"a":[1,13,26,56,77,144,212],"lightweight":[2,62,92,175],"and":[3,32,102,160,239],"robust":[4],"portrait":[5,39,48,93,150],"segmentation":[6,31,40,49,94,151,232],"algorithm":[7],"is":[8,50,208],"an":[9,98,196],"important":[10],"task":[11],"for":[12,229],"wide":[14],"range":[15],"of":[16,28,55,80,135,182],"face":[17],"applications.":[18],"However,":[19],"the":[20,29,47,53,84,89,139,164,180,193,200,234],"problem":[21],"has":[22,41,66],"been":[23,68],"considered":[24],"as":[25],"subset":[27],"object":[30],"less":[33],"handled":[34],"in":[35,52,72,243],"this":[36,73],"field.":[37],"Obviously,":[38],"its":[42],"unique":[43],"requirements.":[44],"First,":[45],"because":[46],"performed":[51],"middle":[54],"whole":[57],"process,":[58],"it":[59],"requires":[60],"extremely":[61,91],"models.":[63,176],"Second,":[64],"there":[65],"not":[67],"any":[69],"public":[70],"datasets":[71],"domain":[74],"that":[75,169,223],"contain":[76],"sufficient":[78],"number":[79,181],"images.":[81],"To":[82,137],"solve":[83],"first":[85],"problem,":[86,141],"we":[87,142,167,221],"introduce":[88],"new":[90],"model":[95,207],"SINet,":[96],"containing":[97],"information":[99,107,117],"blocking":[100,108],"decoder":[101,109],"spatial":[103,116,123],"squeeze":[104,124],"modules.":[105],"The":[106,122,237],"uses":[110,126],"confidence":[111],"estimation":[112],"to":[113,130,147,186],"recover":[114],"local":[115],"without":[118],"spoiling":[119],"global":[120],"consistency.":[121,136],"module":[125],"multiple":[127],"receptive":[128],"fields":[129],"cope":[131],"with":[132,216],"various":[133,173],"sizes":[134],"tackle":[138],"second":[140],"propose":[143],"simple":[145],"method":[146,171,178,225],"create":[148],"additional":[149],"data,":[152],"which":[153],"can":[154,226],"improve":[155],"accuracy.":[156],"In":[157,219],"our":[158,170,206,224],"qualitative":[159],"quantitative":[161],"analysis":[162],"on":[163,211,233],"EG1800":[165],"dataset,":[166],"show":[168,205],"outperforms":[172],"existing":[174],"Our":[177],"reduces":[179],"parameters":[183],"from":[184,199],"2.1M":[185],"86.9K":[187],"(around":[188],"95.9%":[189],"reduction),":[190],"while":[191],"maintaining":[192],"accuracy":[194],"under":[195],"1%":[197],"margin":[198],"state-of-the-art":[201],"method.":[202],"We":[203],"also":[204],"successfully":[209],"executed":[210],"real":[213],"mobile":[214],"device":[215],"100.6":[217],"FPS.":[218],"addition,":[220],"demonstrate":[222],"be":[227],"used":[228],"general":[230],"semantic":[231],"Cityscapes":[235],"dataset.":[236],"code":[238],"dataset":[240],"are":[241],"available":[242],"https://github.com/HYOJINPARK/ExtPortraitSeg.":[244]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":11},{"year":2022,"cited_by_count":9},{"year":2021,"cited_by_count":5},{"year":2020,"cited_by_count":4}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2025-10-10T00:00:00"}