{"id":"https://openalex.org/W2921853515","doi":"https://doi.org/10.1109/cvpr.2019.00271","title":"Structured Knowledge Distillation for Semantic Segmentation","display_name":"Structured Knowledge Distillation for Semantic Segmentation","publication_year":2019,"publication_date":"2019-06-01","ids":{"openalex":"https://openalex.org/W2921853515","doi":"https://doi.org/10.1109/cvpr.2019.00271","mag":"2921853515"},"language":"en","primary_location":{"id":"doi:10.1109/cvpr.2019.00271","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr.2019.00271","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","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/A5100376140","display_name":"Yifan Liu","orcid":"https://orcid.org/0000-0002-2746-8186"},"institutions":[{"id":"https://openalex.org/I5681781","display_name":"University of Adelaide","ror":"https://ror.org/00892tw58","country_code":"AU","type":"education","lineage":["https://openalex.org/I5681781"]}],"countries":["AU"],"is_corresponding":true,"raw_author_name":"Yifan Liu","raw_affiliation_strings":["Univ. of Adelaide","University of Adelaide"],"affiliations":[{"raw_affiliation_string":"Univ. of Adelaide","institution_ids":["https://openalex.org/I5681781"]},{"raw_affiliation_string":"University of Adelaide","institution_ids":["https://openalex.org/I5681781"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100451984","display_name":"Ke Chen","orcid":"https://orcid.org/0000-0001-9457-9364"},"institutions":[{"id":"https://openalex.org/I4210164937","display_name":"Microsoft Research (United Kingdom)","ror":"https://ror.org/05k87vq12","country_code":"GB","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210164937"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Ke Chen","raw_affiliation_strings":["Microsoft","(Microsoft)"],"affiliations":[{"raw_affiliation_string":"Microsoft","institution_ids":["https://openalex.org/I4210164937"]},{"raw_affiliation_string":"(Microsoft)","institution_ids":["https://openalex.org/I4210164937"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103162393","display_name":"Chris Liu","orcid":"https://orcid.org/0009-0003-0045-9970"},"institutions":[{"id":"https://openalex.org/I4210164937","display_name":"Microsoft Research (United Kingdom)","ror":"https://ror.org/05k87vq12","country_code":"GB","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210164937"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Chris Liu","raw_affiliation_strings":["Microsoft","(Microsoft)"],"affiliations":[{"raw_affiliation_string":"Microsoft","institution_ids":["https://openalex.org/I4210164937"]},{"raw_affiliation_string":"(Microsoft)","institution_ids":["https://openalex.org/I4210164937"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5032405950","display_name":"Zengchang Qin","orcid":"https://orcid.org/0000-0002-8084-6721"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zengchang Qin","raw_affiliation_strings":["Intelligent Computing & Machine Learning Lab, School of ASEE, Beihang Univ","Intelligent Computing and Machine Learning Lab School of ASEE, Beihang University"],"affiliations":[{"raw_affiliation_string":"Intelligent Computing & Machine Learning Lab, School of ASEE, Beihang Univ","institution_ids":[]},{"raw_affiliation_string":"Intelligent Computing and Machine Learning Lab School of ASEE, Beihang University","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102328739","display_name":"Zhenbo Luo","orcid":null},"institutions":[{"id":"https://openalex.org/I4210155230","display_name":"Samsung (China)","ror":"https://ror.org/04yt00889","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250650973","https://openalex.org/I4210155230"]},{"id":"https://openalex.org/I2250650973","display_name":"Samsung (South Korea)","ror":"https://ror.org/04w3jy968","country_code":"KR","type":"company","lineage":["https://openalex.org/I2250650973"]}],"countries":["CN","KR"],"is_corresponding":false,"raw_author_name":"Zhenbo Luo","raw_affiliation_strings":["Samsung Research Institute China-Beijing","[Samsung Research Institute - China, Beijing]"],"affiliations":[{"raw_affiliation_string":"Samsung Research Institute China-Beijing","institution_ids":["https://openalex.org/I2250650973"]},{"raw_affiliation_string":"[Samsung Research Institute - China, Beijing]","institution_ids":["https://openalex.org/I4210155230"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5075880303","display_name":"Jingdong Wang","orcid":"https://orcid.org/0000-0002-4888-4445"},"institutions":[{"id":"https://openalex.org/I4210164937","display_name":"Microsoft Research (United Kingdom)","ror":"https://ror.org/05k87vq12","country_code":"GB","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210164937"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Jingdong Wang","raw_affiliation_strings":["Microsoft Research","Microsoft Research#TAB#"],"affiliations":[{"raw_affiliation_string":"Microsoft Research","institution_ids":["https://openalex.org/I4210164937"]},{"raw_affiliation_string":"Microsoft Research#TAB#","institution_ids":["https://openalex.org/I4210164937"]}]}],"institutions":[],"countries_distinct_count":4,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5100376140"],"corresponding_institution_ids":["https://openalex.org/I5681781"],"apc_list":null,"apc_paid":null,"fwci":0.8098,"has_fulltext":false,"cited_by_count":26,"citation_normalized_percentile":{"value":0.76338951,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"2599","last_page":"2608"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9987000226974487,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9987000226974487,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9986000061035156,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T11775","display_name":"COVID-19 diagnosis using AI","score":0.9984999895095825,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/distillation","display_name":"Distillation","score":0.9167158603668213},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7685798406600952},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6902956962585449},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6285884380340576},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6000856161117554},{"id":"https://openalex.org/keywords/structured-prediction","display_name":"Structured prediction","score":0.5376056432723999},{"id":"https://openalex.org/keywords/scheme","display_name":"Scheme (mathematics)","score":0.5039569735527039},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.4876521825790405},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.424746572971344},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.42129021883010864},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.37004733085632324},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3620631694793701},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.20999491214752197},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.13863196969032288}],"concepts":[{"id":"https://openalex.org/C204030448","wikidata":"https://www.wikidata.org/wiki/Q101017","display_name":"Distillation","level":2,"score":0.9167158603668213},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7685798406600952},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6902956962585449},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6285884380340576},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6000856161117554},{"id":"https://openalex.org/C22367795","wikidata":"https://www.wikidata.org/wiki/Q7625208","display_name":"Structured prediction","level":2,"score":0.5376056432723999},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.5039569735527039},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4876521825790405},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.424746572971344},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.42129021883010864},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.37004733085632324},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3620631694793701},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.20999491214752197},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.13863196969032288},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","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},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/cvpr.2019.00271","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr.2019.00271","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","raw_type":"proceedings-article"},{"id":"mag:2921853515","is_oa":false,"landing_page_url":null,"pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":53,"referenced_works":["https://openalex.org/W1745334888","https://openalex.org/W2097117768","https://openalex.org/W2183341477","https://openalex.org/W2194775991","https://openalex.org/W2340897893","https://openalex.org/W2395611524","https://openalex.org/W2412782625","https://openalex.org/W2559597482","https://openalex.org/W2560023338","https://openalex.org/W2563705555","https://openalex.org/W2737258237","https://openalex.org/W2740036778","https://openalex.org/W2750432752","https://openalex.org/W2778955544","https://openalex.org/W2799166040","https://openalex.org/W2799213142","https://openalex.org/W2804918468","https://openalex.org/W2963125010","https://openalex.org/W2963163009","https://openalex.org/W2963420272","https://openalex.org/W2963446712","https://openalex.org/W2963881378","https://openalex.org/W2964031641","https://openalex.org/W2964297864","https://openalex.org/W6637551013","https://openalex.org/W6638523607","https://openalex.org/W6639824700","https://openalex.org/W6639824712","https://openalex.org/W6640295612","https://openalex.org/W6678815747","https://openalex.org/W6679909955","https://openalex.org/W6695314431","https://openalex.org/W6696085341","https://openalex.org/W6697590335","https://openalex.org/W6702130928","https://openalex.org/W6713645886","https://openalex.org/W6717372056","https://openalex.org/W6727862155","https://openalex.org/W6729856380","https://openalex.org/W6730179637","https://openalex.org/W6735913928","https://openalex.org/W6737324727","https://openalex.org/W6737664043","https://openalex.org/W6740745780","https://openalex.org/W6745560452","https://openalex.org/W6748481559","https://openalex.org/W6749046737","https://openalex.org/W6752378368","https://openalex.org/W6753421600","https://openalex.org/W6754713557","https://openalex.org/W6754879843","https://openalex.org/W6755536945","https://openalex.org/W6757855356"],"related_works":["https://openalex.org/W2980323420","https://openalex.org/W2952787292","https://openalex.org/W3001025759","https://openalex.org/W3103739166","https://openalex.org/W3197209914","https://openalex.org/W3150112631","https://openalex.org/W3160188956","https://openalex.org/W3103972209","https://openalex.org/W3207288552","https://openalex.org/W2963402808","https://openalex.org/W2972594701","https://openalex.org/W3212602335","https://openalex.org/W3201102618","https://openalex.org/W2795080186","https://openalex.org/W2927560389","https://openalex.org/W2887783173","https://openalex.org/W2735033384","https://openalex.org/W3093392754","https://openalex.org/W3101469729","https://openalex.org/W2363975424"],"abstract_inverted_index":{"In":[0],"this":[1],"paper,":[2],"we":[3],"investigate":[4],"the":[5,25,32,53,66,88],"issue":[6],"of":[7,19,105],"knowledge":[8,42,55,107],"distillation":[9,33,43,81,85,94,108],"for":[10,37,44],"training":[11,98],"compact":[12,60],"semantic":[13,69],"segmentation":[14,70],"networks":[15,58],"by":[16,65,112],"making":[17],"use":[18],"cumbersome":[20,57],"networks.":[21],"We":[22,48,76],"start":[23],"from":[24,56],"straightforward":[26],"scheme,":[27],"pixel-wise":[28],"distillation,":[29],"which":[30,62],"applies":[31],"scheme":[34],"originally":[35],"introduced":[36],"image":[38],"classification":[39],"and":[40,91,122],"performs":[41],"each":[45],"pixel":[46],"separately.":[47],"further":[49],"propose":[50],"to":[51,99],"distill":[52,100],"structured":[54,73,80],"into":[59],"networks,":[61],"is":[63,71,110],"motivated":[64],"fact":[67],"that":[68,86,95],"a":[72],"prediction":[74],"problem.":[75],"study":[77],"two":[78],"such":[79],"schemes:":[82],"(i)":[83],"pair-wise":[84],"distills":[87],"pairwise":[89],"similarities,":[90],"(ii)":[92],"holistic":[93,101],"uses":[96],"adversarial":[97],"knowledge.":[102],"The":[103],"effectiveness":[104],"our":[106],"approaches":[109],"demonstrated":[111],"extensive":[113],"experiments":[114],"on":[115],"three":[116],"scene":[117],"parsing":[118],"datasets:":[119],"Cityscapes,":[120],"Camvid":[121],"ADE20K.":[123]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":7},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}