{"id":"https://openalex.org/W3090344056","doi":"https://doi.org/10.1109/ijcnn48605.2020.9207197","title":"Robust Semi-Supervised Semantic Segmentation Based on Self-Attention and Spectral Normalization","display_name":"Robust Semi-Supervised Semantic Segmentation Based on Self-Attention and Spectral Normalization","publication_year":2020,"publication_date":"2020-07-01","ids":{"openalex":"https://openalex.org/W3090344056","doi":"https://doi.org/10.1109/ijcnn48605.2020.9207197","mag":"3090344056"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn48605.2020.9207197","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn48605.2020.9207197","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 International Joint Conference on Neural Networks (IJCNN)","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/A5012709709","display_name":"Jia Zhang","orcid":"https://orcid.org/0000-0002-2513-4115"},"institutions":[{"id":"https://openalex.org/I29739308","display_name":"Guangxi Normal University","ror":"https://ror.org/02frt9q65","country_code":"CN","type":"education","lineage":["https://openalex.org/I29739308"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jia Zhang","raw_affiliation_strings":["Guangxi Key Lab of Multi-source Information Mining and Security, Guangxi Normal University, Guilin, China"],"affiliations":[{"raw_affiliation_string":"Guangxi Key Lab of Multi-source Information Mining and Security, Guangxi Normal University, Guilin, China","institution_ids":["https://openalex.org/I29739308"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100701695","display_name":"Zhixin Li","orcid":"https://orcid.org/0000-0002-5313-6134"},"institutions":[{"id":"https://openalex.org/I29739308","display_name":"Guangxi Normal University","ror":"https://ror.org/02frt9q65","country_code":"CN","type":"education","lineage":["https://openalex.org/I29739308"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhixin Li","raw_affiliation_strings":["Guangxi Key Lab of Multi-source Information Mining and Security, Guangxi Normal University, Guilin, China"],"affiliations":[{"raw_affiliation_string":"Guangxi Key Lab of Multi-source Information Mining and Security, Guangxi Normal University, Guilin, China","institution_ids":["https://openalex.org/I29739308"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016469515","display_name":"Canlong Zhang","orcid":"https://orcid.org/0000-0003-4375-1405"},"institutions":[{"id":"https://openalex.org/I29739308","display_name":"Guangxi Normal University","ror":"https://ror.org/02frt9q65","country_code":"CN","type":"education","lineage":["https://openalex.org/I29739308"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Canlong Zhang","raw_affiliation_strings":["Guangxi Key Lab of Multi-source Information Mining and Security, Guangxi Normal University, Guilin, China"],"affiliations":[{"raw_affiliation_string":"Guangxi Key Lab of Multi-source Information Mining and Security, Guangxi Normal University, Guilin, China","institution_ids":["https://openalex.org/I29739308"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5053794277","display_name":"Huifang Ma","orcid":"https://orcid.org/0000-0002-5104-8982"},"institutions":[{"id":"https://openalex.org/I68986083","display_name":"Northwest Normal University","ror":"https://ror.org/00gx3j908","country_code":"CN","type":"education","lineage":["https://openalex.org/I68986083"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Huifang Ma","raw_affiliation_strings":["College of Computer Science and Engineering, Northwest Normal University, Lanzhou, China"],"affiliations":[{"raw_affiliation_string":"College of Computer Science and Engineering, Northwest Normal University, Lanzhou, China","institution_ids":["https://openalex.org/I68986083"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5012709709"],"corresponding_institution_ids":["https://openalex.org/I29739308"],"apc_list":null,"apc_paid":null,"fwci":0.1887,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.57548487,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13114","display_name":"Image Processing Techniques and Applications","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T13114","display_name":"Image Processing Techniques and Applications","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12549","display_name":"Image and Object Detection Techniques","score":0.9968000054359436,"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":0.9962999820709229,"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/discriminator","display_name":"Discriminator","score":0.902799129486084},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8139758110046387},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7590982913970947},{"id":"https://openalex.org/keywords/normalization","display_name":"Normalization (sociology)","score":0.6647564768791199},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6610919833183289},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6019352078437805},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.556673526763916},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.48874425888061523},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3449559211730957}],"concepts":[{"id":"https://openalex.org/C2779803651","wikidata":"https://www.wikidata.org/wiki/Q5282088","display_name":"Discriminator","level":3,"score":0.902799129486084},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8139758110046387},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7590982913970947},{"id":"https://openalex.org/C136886441","wikidata":"https://www.wikidata.org/wiki/Q926129","display_name":"Normalization (sociology)","level":2,"score":0.6647564768791199},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6610919833183289},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6019352078437805},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.556673526763916},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.48874425888061523},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3449559211730957},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.0},{"id":"https://openalex.org/C19165224","wikidata":"https://www.wikidata.org/wiki/Q23404","display_name":"Anthropology","level":1,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/ijcnn48605.2020.9207197","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn48605.2020.9207197","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},{"id":"pmh:oai:ir.lzu.edu.cn/:262010/441821","is_oa":false,"landing_page_url":"http://ir.lzu.edu.cn/handle/262010/441821","pdf_url":null,"source":{"id":"https://openalex.org/S4406923049","display_name":"Lanzhou University Institutional Repository","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"\u4f1a\u8bae\u8bba\u6587"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","score":0.7200000286102295,"id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":53,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1686810756","https://openalex.org/W1861492603","https://openalex.org/W1901129140","https://openalex.org/W1903029394","https://openalex.org/W1923697677","https://openalex.org/W2031489346","https://openalex.org/W2099471712","https://openalex.org/W2124592697","https://openalex.org/W2144794286","https://openalex.org/W2150066425","https://openalex.org/W2173520492","https://openalex.org/W2194775991","https://openalex.org/W2340897893","https://openalex.org/W2412782625","https://openalex.org/W2560023338","https://openalex.org/W2597655663","https://openalex.org/W2623546809","https://openalex.org/W2785678896","https://openalex.org/W2787241931","https://openalex.org/W2798791840","https://openalex.org/W2895340641","https://openalex.org/W2948400535","https://openalex.org/W2962737447","https://openalex.org/W2963403868","https://openalex.org/W2963563573","https://openalex.org/W2963684088","https://openalex.org/W2963727650","https://openalex.org/W2963836885","https://openalex.org/W2963840672","https://openalex.org/W2963956526","https://openalex.org/W2964121744","https://openalex.org/W2964189376","https://openalex.org/W2964201867","https://openalex.org/W2989484703","https://openalex.org/W2995808743","https://openalex.org/W4289097569","https://openalex.org/W4320013936","https://openalex.org/W4385245566","https://openalex.org/W6631190155","https://openalex.org/W6637373629","https://openalex.org/W6639102338","https://openalex.org/W6639824700","https://openalex.org/W6640295612","https://openalex.org/W6685352114","https://openalex.org/W6735377749","https://openalex.org/W6739901393","https://openalex.org/W6748582592","https://openalex.org/W6748692255","https://openalex.org/W6750703173","https://openalex.org/W6754713557","https://openalex.org/W6756603004","https://openalex.org/W6757378423"],"related_works":["https://openalex.org/W4293202849","https://openalex.org/W1980965563","https://openalex.org/W1489300767","https://openalex.org/W2387995142","https://openalex.org/W4380714744","https://openalex.org/W4319453655","https://openalex.org/W2089959425","https://openalex.org/W2057775761","https://openalex.org/W4321441197","https://openalex.org/W1522196789"],"abstract_inverted_index":{"The":[0,66,138],"application":[1],"of":[2,20,33,80,93,109,119,161],"adversarial":[3,39,142],"learning":[4,143],"for":[5,86],"semi-supervised":[6,144,163],"semantic":[7,145,166],"image":[8,54,96,146,154,167],"segmentation":[9,147,155,168],"based":[10,99],"on":[11,100],"convolutional":[12,64],"neural":[13],"networks":[14],"can":[15,56],"effectively":[16,84],"reduce":[17],"the":[18,26,30,34,37,49,78,81,94,107,110,117,131,135,159],"number":[19],"manually":[21],"generated":[22],"labels":[23],"required":[24],"in":[25,36,77],"training":[27,121,136],"process.":[28,137],"However,":[29],"convolution":[31],"operator":[32],"generator":[35,79],"generative":[38],"network":[40,148],"(GAN)":[41],"has":[42,112],"a":[43,74],"local":[44],"receptive":[45],"field,":[46],"so":[47],"that":[48],"long-range":[50],"dependencies":[51],"between":[52,88],"different":[53],"regions":[55,92],"only":[57],"be":[58],"modeled":[59],"after":[60],"passing":[61],"through":[62],"multiple":[63],"layers.":[65],"present":[67],"work":[68],"addresses":[69],"this":[70],"issue":[71],"by":[72,126],"introducing":[73],"self-attention":[75,141],"mechanism":[76],"GAN":[82,120,132],"to":[83,115,130,151],"account":[85],"relationships":[87],"widely":[89],"separated":[90],"spatial":[91],"input":[95],"with":[97,158],"supervision":[98],"pixel-level":[101],"ground":[102],"truth":[103],"data.":[104],"In":[105],"addition,":[106],"adjustment":[108],"discriminator":[111,133],"been":[113],"demonstrated":[114,150],"affect":[116],"stability":[118],"performance.":[122],"This":[123],"is":[124,149],"addressed":[125],"applying":[127],"spectral":[128],"normalization":[129],"during":[134],"proposed":[139],"stable":[140],"provide":[152],"superior":[153],"performance":[156],"compared":[157],"results":[160],"current":[162],"and":[164],"fully-supervised":[165],"techniques.":[169]},"counts_by_year":[{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
