{"id":"https://openalex.org/W4416965989","doi":"https://doi.org/10.1109/tmm.2025.3639923","title":"Triple-View Knowledge Distillation for Semi-Supervised Semantic Segmentation","display_name":"Triple-View Knowledge Distillation for Semi-Supervised Semantic Segmentation","publication_year":2025,"publication_date":"2025-12-03","ids":{"openalex":"https://openalex.org/W4416965989","doi":"https://doi.org/10.1109/tmm.2025.3639923"},"language":null,"primary_location":{"id":"doi:10.1109/tmm.2025.3639923","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tmm.2025.3639923","pdf_url":null,"source":{"id":"https://openalex.org/S137030581","display_name":"IEEE Transactions on Multimedia","issn_l":"1520-9210","issn":["1520-9210","1941-0077"],"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 Multimedia","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/A5100741315","display_name":"Ping Li","orcid":"https://orcid.org/0000-0002-8515-7773"},"institutions":[{"id":"https://openalex.org/I50760025","display_name":"Hangzhou Dianzi University","ror":"https://ror.org/0576gt767","country_code":"CN","type":"education","lineage":["https://openalex.org/I50760025"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ping Li","raw_affiliation_strings":["School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China"],"raw_orcid":"https://orcid.org/0000-0002-8515-7773","affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China","institution_ids":["https://openalex.org/I50760025"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5033905932","display_name":"Junjie Chen","orcid":"https://orcid.org/0000-0002-3536-3501"},"institutions":[{"id":"https://openalex.org/I50760025","display_name":"Hangzhou Dianzi University","ror":"https://ror.org/0576gt767","country_code":"CN","type":"education","lineage":["https://openalex.org/I50760025"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Junjie Chen","raw_affiliation_strings":["School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China","institution_ids":["https://openalex.org/I50760025"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5116493713","display_name":"Li Yuan","orcid":"https://orcid.org/0000-0002-2120-5588"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Li Yuan","raw_affiliation_strings":["School of Electronic and Computer Engineering, Peking University, China"],"raw_orcid":"https://orcid.org/0000-0002-2120-5588","affiliations":[{"raw_affiliation_string":"School of Electronic and Computer Engineering, Peking University, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5026554755","display_name":"Xianghua Xu","orcid":"https://orcid.org/0000-0001-9832-5804"},"institutions":[{"id":"https://openalex.org/I50760025","display_name":"Hangzhou Dianzi University","ror":"https://ror.org/0576gt767","country_code":"CN","type":"education","lineage":["https://openalex.org/I50760025"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xianghua Xu","raw_affiliation_strings":["School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China"],"raw_orcid":"https://orcid.org/0000-0001-9832-5804","affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China","institution_ids":["https://openalex.org/I50760025"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5042449002","display_name":"Mingli Song","orcid":null},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Mingli Song","raw_affiliation_strings":["College of Computer Science, Zhejiang University, Hangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Computer Science, Zhejiang University, Hangzhou, China","institution_ids":["https://openalex.org/I76130692"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.35091007,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"28","issue":null,"first_page":"1511","last_page":"1522"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9343000054359436,"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":0.9343000054359436,"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":0.02889999933540821,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.006599999964237213,"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/encoder","display_name":"Encoder","score":0.6711999773979187},{"id":"https://openalex.org/keywords/pascal","display_name":"Pascal (unit)","score":0.5946000218391418},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5935999751091003},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5795999765396118},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.5440000295639038},{"id":"https://openalex.org/keywords/concatenation","display_name":"Concatenation (mathematics)","score":0.5098000168800354},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.47519999742507935},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.39100000262260437},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.38909998536109924}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8985999822616577},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.6711999773979187},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6029999852180481},{"id":"https://openalex.org/C75608658","wikidata":"https://www.wikidata.org/wiki/Q44395","display_name":"Pascal (unit)","level":2,"score":0.5946000218391418},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5935999751091003},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5795999765396118},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.5440000295639038},{"id":"https://openalex.org/C87619178","wikidata":"https://www.wikidata.org/wiki/Q126002","display_name":"Concatenation (mathematics)","level":2,"score":0.5098000168800354},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.47519999742507935},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4293000102043152},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.39100000262260437},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.38909998536109924},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3873000144958496},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.3700999915599823},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.33899998664855957},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.32350000739097595},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2935999929904938},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.28790000081062317},{"id":"https://openalex.org/C157899210","wikidata":"https://www.wikidata.org/wiki/Q1395022","display_name":"Convolutional code","level":3,"score":0.28049999475479126},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.27300000190734863},{"id":"https://openalex.org/C2781122975","wikidata":"https://www.wikidata.org/wiki/Q16928266","display_name":"Semantic feature","level":2,"score":0.27079999446868896},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.2676999866962433},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.2635999917984009},{"id":"https://openalex.org/C77637269","wikidata":"https://www.wikidata.org/wiki/Q7002051","display_name":"Neural coding","level":2,"score":0.26019999384880066},{"id":"https://openalex.org/C204030448","wikidata":"https://www.wikidata.org/wiki/Q101017","display_name":"Distillation","level":2,"score":0.2597000002861023},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.25940001010894775},{"id":"https://openalex.org/C2780586882","wikidata":"https://www.wikidata.org/wiki/Q7520643","display_name":"Simple (philosophy)","level":2,"score":0.2551000118255615},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.2529999911785126}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tmm.2025.3639923","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tmm.2025.3639923","pdf_url":null,"source":{"id":"https://openalex.org/S137030581","display_name":"IEEE Transactions on Multimedia","issn_l":"1520-9210","issn":["1520-9210","1941-0077"],"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 Multimedia","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G8504279988","display_name":null,"funder_award_id":"62202014","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":57,"referenced_works":["https://openalex.org/W1677182931","https://openalex.org/W1824737917","https://openalex.org/W1901129140","https://openalex.org/W1903029394","https://openalex.org/W2037227137","https://openalex.org/W2047188941","https://openalex.org/W2108598243","https://openalex.org/W2125408827","https://openalex.org/W2144794286","https://openalex.org/W2194775991","https://openalex.org/W2340897893","https://openalex.org/W2412782625","https://openalex.org/W2507296351","https://openalex.org/W2533214323","https://openalex.org/W2560023338","https://openalex.org/W2565639579","https://openalex.org/W2787091153","https://openalex.org/W2808139377","https://openalex.org/W2884822772","https://openalex.org/W2962804657","https://openalex.org/W2962949934","https://openalex.org/W2963091558","https://openalex.org/W3035006120","https://openalex.org/W3035574168","https://openalex.org/W3035680157","https://openalex.org/W3107695429","https://openalex.org/W3127902700","https://openalex.org/W3170841864","https://openalex.org/W3171581326","https://openalex.org/W3195975500","https://openalex.org/W3197957534","https://openalex.org/W3199130398","https://openalex.org/W3202277597","https://openalex.org/W4214899942","https://openalex.org/W4221161877","https://openalex.org/W4225343834","https://openalex.org/W4312310512","https://openalex.org/W4312671791","https://openalex.org/W4319878483","https://openalex.org/W4362586367","https://openalex.org/W4382240924","https://openalex.org/W4382371042","https://openalex.org/W4385245566","https://openalex.org/W4385453232","https://openalex.org/W4386065584","https://openalex.org/W4386076267","https://openalex.org/W4386076563","https://openalex.org/W4387849426","https://openalex.org/W4388073520","https://openalex.org/W4389473983","https://openalex.org/W4392543845","https://openalex.org/W4396733260","https://openalex.org/W4398224523","https://openalex.org/W4401414214","https://openalex.org/W4402568933","https://openalex.org/W4402727392","https://openalex.org/W4402727556"],"related_works":[],"abstract_inverted_index":{"To":[0],"alleviate":[1],"the":[2,24,29,43,101,124,128,141,160,164,182],"expensive":[3],"human":[4],"labeling":[5],"problem,":[6],"semi-supervised":[7,155],"semantic":[8,156],"segmentation":[9],"utilizes":[10],"a":[11,65,105,114,132,147,188],"few":[12],"labeled":[13],"images":[14,21],"along":[15],"with":[16,28,38,42,71],"an":[17],"abundance":[18],"of":[19,184],"unlabeled":[20],"to":[22,51,60,68,74,82,112,139],"predict":[23],"pixel-level":[25],"label":[26],"maps":[27],"same":[30,44],"size.":[31],"Previous":[32],"methods":[33],"often":[34],"rely":[35],"on":[36,170],"co-training":[37],"two":[39,171],"convolutional":[40],"networks":[41],"architecture":[45],"but":[46],"different":[47,72],"initialization,":[48],"which":[49],"fails":[50],"capture":[52,83],"sufficiently":[53],"diverse":[54,76],"features.":[55],"This":[56,109],"limitation":[57],"motivates":[58],"us":[59,111],"employ":[61],"tri-training":[62],"and":[63,98,100,163,179,193],"design":[64],"triple-view":[66,161],"encoder":[67,97,162],"utilize":[69],"encoders":[70],"architectures":[73],"derive":[75],"features,":[77],"while":[78],"leveraging":[79],"knowledge":[80],"distillation":[81],"complementary":[84],"semantics":[85],"among":[86],"these":[87],"encoders.":[88],"Moreover,":[89],"existing":[90],"approaches":[91],"simply":[92],"concatenate":[93],"features":[94,121,126],"from":[95],"both":[96],"decoder,":[99],"simple":[102],"concatenation":[103],"requires":[104],"large":[106],"memory":[107],"cost.":[108],"inspires":[110],"present":[113],"dual-frequency":[115,133,165],"decoder":[116],"that":[117],"selects":[118],"those":[119],"important":[120],"by":[122],"projecting":[123],"spatial-domain":[125],"into":[127],"frequency":[129],"domain,":[130],"where":[131],"channel":[134],"attention":[135],"mechanism":[136],"is":[137,198],"applied":[138],"evaluate":[140],"feature":[142],"importance.":[143],"Therefore,":[144],"we":[145],"propose":[146],"Triple-view":[148],"Knowledge":[149],"Distillation":[150],"framework,":[151],"termed":[152],"TriKD,":[153],"for":[154],"segmentation.":[157],"It":[158],"comprises":[159],"decoder.":[166],"Extensive":[167],"experiments":[168],"conducted":[169],"benchmarks,":[172],"<italic":[173],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[174],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">i.e.</i>,":[175],"Pascal":[176],"VOC":[177],"2012":[178],"Cityscapes,":[180],"validate":[181],"superiority":[183],"our":[185],"method,":[186],"achieving":[187],"satisfying":[189],"tradeoff":[190],"between":[191],"precision":[192],"inference":[194],"speed.":[195],"Our":[196],"code":[197],"available":[199],"at":[200],"GitHub.":[201]},"counts_by_year":[],"updated_date":"2026-03-09T07:00:12.390032","created_date":"2025-12-03T00:00:00"}