{"id":"https://openalex.org/W4295046678","doi":"https://doi.org/10.1109/tmm.2022.3205441","title":"Contour-Aware Equipotential Learning for Semantic Segmentation","display_name":"Contour-Aware Equipotential Learning for Semantic Segmentation","publication_year":2022,"publication_date":"2022-09-09","ids":{"openalex":"https://openalex.org/W4295046678","doi":"https://doi.org/10.1109/tmm.2022.3205441"},"language":"es","primary_location":{"id":"doi:10.1109/tmm.2022.3205441","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tmm.2022.3205441","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/A5021977967","display_name":"Xu Yin","orcid":"https://orcid.org/0000-0003-1967-7030"},"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":"Xu Yin","raw_affiliation_strings":["School of Computing, Korea Advanced Institute of Science and Technology, Daejeon, South Korea"],"raw_orcid":"https://orcid.org/0000-0003-1967-7030","affiliations":[{"raw_affiliation_string":"School of Computing, Korea Advanced Institute of Science and Technology, Daejeon, South Korea","institution_ids":["https://openalex.org/I157485424"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5037006973","display_name":"Dongbo Min","orcid":"https://orcid.org/0000-0003-4825-5240"},"institutions":[{"id":"https://openalex.org/I138925566","display_name":"Ewha Womans University","ror":"https://ror.org/053fp5c05","country_code":"KR","type":"education","lineage":["https://openalex.org/I138925566"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Dongbo Min","raw_affiliation_strings":["Department of Computer Science and Engineering, Ewha Womans University, Seoul, South Korea"],"raw_orcid":"https://orcid.org/0000-0003-4825-5240","affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, Ewha Womans University, Seoul, South Korea","institution_ids":["https://openalex.org/I138925566"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5081150377","display_name":"Yuchi Huo","orcid":"https://orcid.org/0000-0003-3296-7999"},"institutions":[{"id":"https://openalex.org/I4210123185","display_name":"Zhejiang Lab","ror":"https://ror.org/02m2h7991","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210123185"]},{"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":"Yuchi Huo","raw_affiliation_strings":["Zhejiang Laboratory, China and State Key Laboratory of CAD&CG, Zhejiang University, China, Hangzhou, China","Zhejiang Laboratory, Hangzhou, China"],"raw_orcid":"https://orcid.org/0000-0003-3296-7999","affiliations":[{"raw_affiliation_string":"Zhejiang Laboratory, China and State Key Laboratory of CAD&CG, Zhejiang University, China, Hangzhou, China","institution_ids":["https://openalex.org/I76130692","https://openalex.org/I4210123185"]},{"raw_affiliation_string":"Zhejiang Laboratory, Hangzhou, China","institution_ids":["https://openalex.org/I4210123185"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5078173428","display_name":"Sung\u2010Eui Yoon","orcid":"https://orcid.org/0000-0002-7123-1119"},"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":"Sung-Eui Yoon","raw_affiliation_strings":["Faculty of School of Computing, Korea Advanced Institute of Science and Technology, Daejeon, South Korea"],"raw_orcid":"https://orcid.org/0000-0002-7123-1119","affiliations":[{"raw_affiliation_string":"Faculty of School of Computing, Korea Advanced Institute of Science and Technology, Daejeon, South Korea","institution_ids":["https://openalex.org/I157485424"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.0153,"has_fulltext":false,"cited_by_count":10,"citation_normalized_percentile":{"value":0.77137441,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":98},"biblio":{"volume":"25","issue":null,"first_page":"6146","last_page":"6156"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9993000030517578,"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":0.9993000030517578,"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":0.9993000030517578,"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/T11606","display_name":"Infrastructure Maintenance and Monitoring","score":0.9983000159263611,"subfield":{"id":"https://openalex.org/subfields/2205","display_name":"Civil and Structural Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7701188325881958},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6953325867652893},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6601939797401428},{"id":"https://openalex.org/keywords/boundary","display_name":"Boundary (topology)","score":0.4995448589324951},{"id":"https://openalex.org/keywords/ground-truth","display_name":"Ground truth","score":0.49127739667892456},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.4661371111869812},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.4455568790435791},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.43774065375328064},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.42588457465171814},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3490736484527588},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.32713446021080017},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1416548192501068},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.1318608522415161}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7701188325881958},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6953325867652893},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6601939797401428},{"id":"https://openalex.org/C62354387","wikidata":"https://www.wikidata.org/wiki/Q875399","display_name":"Boundary (topology)","level":2,"score":0.4995448589324951},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.49127739667892456},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4661371111869812},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.4455568790435791},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.43774065375328064},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.42588457465171814},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3490736484527588},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.32713446021080017},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1416548192501068},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.1318608522415161},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tmm.2022.3205441","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tmm.2022.3205441","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":[{"id":"https://metadata.un.org/sdg/9","score":0.5,"display_name":"Industry, innovation and infrastructure"}],"awards":[{"id":"https://openalex.org/G6909019957","display_name":null,"funder_award_id":"2019R1A2C3002833","funder_id":"https://openalex.org/F4320328359","funder_display_name":"Ministry of Science and ICT, South Korea"},{"id":"https://openalex.org/G8317885230","display_name":null,"funder_award_id":"2021R1A4A1032582","funder_id":"https://openalex.org/F4320328359","funder_display_name":"Ministry of Science and ICT, South Korea"}],"funders":[{"id":"https://openalex.org/F4320328359","display_name":"Ministry of Science and ICT, South Korea","ror":"https://ror.org/01wpjm123"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":48,"referenced_works":["https://openalex.org/W1901129140","https://openalex.org/W1903029394","https://openalex.org/W2152237522","https://openalex.org/W2194775991","https://openalex.org/W2340897893","https://openalex.org/W2412782625","https://openalex.org/W2470139095","https://openalex.org/W2560023338","https://openalex.org/W2734349601","https://openalex.org/W2799166040","https://openalex.org/W2885139206","https://openalex.org/W2885343725","https://openalex.org/W2895340641","https://openalex.org/W2949582403","https://openalex.org/W2962716426","https://openalex.org/W2962850830","https://openalex.org/W2963351448","https://openalex.org/W2963881378","https://openalex.org/W2964137095","https://openalex.org/W2964309882","https://openalex.org/W2966434031","https://openalex.org/W2972009374","https://openalex.org/W2981669902","https://openalex.org/W2981689412","https://openalex.org/W2982631194","https://openalex.org/W2991471181","https://openalex.org/W2998663558","https://openalex.org/W3023138874","https://openalex.org/W3034355852","https://openalex.org/W3034826836","https://openalex.org/W3035358681","https://openalex.org/W3041273049","https://openalex.org/W3043238202","https://openalex.org/W3096653763","https://openalex.org/W3097762837","https://openalex.org/W3107113572","https://openalex.org/W3127595489","https://openalex.org/W3168227666","https://openalex.org/W3170019900","https://openalex.org/W3183229916","https://openalex.org/W4225935108","https://openalex.org/W4297775537","https://openalex.org/W6639824700","https://openalex.org/W6713128830","https://openalex.org/W6737664043","https://openalex.org/W6748481559","https://openalex.org/W6754713557","https://openalex.org/W6755308174"],"related_works":["https://openalex.org/W4295532600","https://openalex.org/W2063823869","https://openalex.org/W2047973478","https://openalex.org/W2067569035","https://openalex.org/W4375867731","https://openalex.org/W2090985514","https://openalex.org/W2611989081","https://openalex.org/W2890372105","https://openalex.org/W1997160662","https://openalex.org/W1522196789"],"abstract_inverted_index":{"With":[0],"increasing":[1],"demands":[2],"for":[3,190],"high-quality":[4],"semantic":[5,11,48,176],"segmentation":[6,131,143],"in":[7,109],"the":[8,37,46,67,84,89,93,110,136,141,162,168,185],"industry,":[9],"hard-distinguishing":[10],"boundaries":[12,60],"have":[13],"posed":[14],"a":[15,51,73],"significant":[16],"threat":[17],"to":[18,30,50,55,66,119,139],"existing":[19,130],"solutions.":[20],"Inspired":[21],"by":[22],"real-life":[23],"experience,":[24],"i.e.,":[25],"combining":[26],"varied":[27],"observations":[28],"contributes":[29],"higher":[31],"visual":[32],"recognition":[33],"confidence,":[34],"we":[35],"present":[36],"equipotential":[38,94],"learning":[39],"(EPL)":[40],"method.":[41],"This":[42,133],"novel":[43],"module":[44,165],"transfers":[45],"predicted/ground-truth":[47],"labels":[49],"self-defined":[52],"potential":[53,68],"domain":[54,69],"learn":[56],"and":[57,92,101,122,148,158,182,193],"infer":[58],"decision":[59],"along":[61],"customized":[62],"directions.":[63],"The":[64],"conversion":[65],"is":[70,117,135],"implemented":[71],"via":[72],"lightweight":[74],"differentiable":[75],"anisotropic":[76,98],"convolution":[77],"without":[78],"incurring":[79],"any":[80],"parameter":[81],"overhead.":[82],"Besides,":[83,179],"designed":[85],"two":[86],"loss":[87,91,96],"functions,":[88],"point":[90],"line":[95],"implement":[97],"field":[99,146],"regression":[100,147],"category-level":[102],"contour":[103,149],"learning,":[104],"respectively,":[105],"enhancing":[106],"prediction":[107],"consistencies":[108],"inter/intra-class":[111],"boundary":[112,142,177],"areas.":[113,178],"More":[114],"importantly,":[115],"EPL":[116,164,189],"agnostic":[118],"network":[120,172],"architectures,":[121],"thus":[123],"it":[124],"can":[125,166],"be":[126],"plugged":[127],"into":[128],"most":[129],"models.":[132],"paper":[134],"first":[137],"attempt":[138],"address":[140],"problem":[144],"with":[145],"learning.":[150],"Meaningful":[151],"performance":[152],"improvements":[153],"on":[154],"Pascal":[155],"Voc":[156],"2012":[157],"Cityscapes":[159],"demonstrate":[160],"that":[161],"proposed":[163],"benefit":[167],"off-the-shelf":[169],"fully":[170],"convolutional":[171],"models":[173],"when":[174],"recognizing":[175],"intensive":[180],"comparisons":[181],"analysis":[183],"show":[184],"favorable":[186],"merits":[187],"of":[188],"distinguishing":[191],"semantically-similar":[192],"irregular-shaped":[194],"categories.":[195]},"counts_by_year":[{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":2}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}