{"id":"https://openalex.org/W2972781556","doi":"https://doi.org/10.1109/icarm.2019.8833973","title":"Semantic Segmentation via Structured Refined Prediction and Dual Global Priors","display_name":"Semantic Segmentation via Structured Refined Prediction and Dual Global Priors","publication_year":2019,"publication_date":"2019-07-01","ids":{"openalex":"https://openalex.org/W2972781556","doi":"https://doi.org/10.1109/icarm.2019.8833973","mag":"2972781556"},"language":"en","primary_location":{"id":"doi:10.1109/icarm.2019.8833973","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icarm.2019.8833973","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE 4th International Conference on Advanced Robotics and Mechatronics (ICARM)","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/A5100336450","display_name":"Long Chen","orcid":"https://orcid.org/0000-0003-4925-0572"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Long Chen","raw_affiliation_strings":["School of Data and Computer Science, Sun Yat-sen University, Guangzhou, Guangdong, P.R.China"],"affiliations":[{"raw_affiliation_string":"School of Data and Computer Science, Sun Yat-sen University, Guangzhou, Guangdong, P.R.China","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5059542928","display_name":"Wujing Zhan","orcid":"https://orcid.org/0000-0001-9683-7996"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wujing Zhan","raw_affiliation_strings":["School of Data and Computer Science, Sun Yat-sen University, Guangzhou, Guangdong, P.R.China"],"affiliations":[{"raw_affiliation_string":"School of Data and Computer Science, Sun Yat-sen University, Guangzhou, Guangdong, P.R.China","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101940850","display_name":"Jiajie Liu","orcid":"https://orcid.org/0000-0002-7651-5540"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiajie Liu","raw_affiliation_strings":["School of Data and Computer Science, Sun Yat-sen University, Guangzhou, Guangdong, P.R.China"],"affiliations":[{"raw_affiliation_string":"School of Data and Computer Science, Sun Yat-sen University, Guangzhou, Guangdong, P.R.China","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5086501723","display_name":"Wei Tian","orcid":"https://orcid.org/0000-0002-5085-7219"},"institutions":[{"id":"https://openalex.org/I102335020","display_name":"Karlsruhe Institute of Technology","ror":"https://ror.org/04t3en479","country_code":"DE","type":"education","lineage":["https://openalex.org/I102335020","https://openalex.org/I1305996414"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Wei Tian","raw_affiliation_strings":["Institute of Measurement and Control Systems, Karlsruhe Institute of Technology, Karlsruhe, Germany"],"affiliations":[{"raw_affiliation_string":"Institute of Measurement and Control Systems, Karlsruhe Institute of Technology, Karlsruhe, Germany","institution_ids":["https://openalex.org/I102335020"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5009572966","display_name":"Dongpu Cao","orcid":"https://orcid.org/0000-0003-2541-5272"},"institutions":[{"id":"https://openalex.org/I151746483","display_name":"University of Waterloo","ror":"https://ror.org/01aff2v68","country_code":"CA","type":"education","lineage":["https://openalex.org/I151746483"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Dongpu Cao","raw_affiliation_strings":["Department of Mechanical and Mechatronics Engineering, University of Waterloo, Ontario, Canada"],"affiliations":[{"raw_affiliation_string":"Department of Mechanical and Mechatronics Engineering, University of Waterloo, Ontario, Canada","institution_ids":["https://openalex.org/I151746483"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5100336450"],"corresponding_institution_ids":["https://openalex.org/I157773358"],"apc_list":null,"apc_paid":null,"fwci":0.1012,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.43956553,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":93},"biblio":{"volume":"2","issue":null,"first_page":"53","last_page":"58"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","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/T10036","display_name":"Advanced Neural Network Applications","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/T11606","display_name":"Infrastructure Maintenance and Monitoring","score":0.9954000115394592,"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"}},{"id":"https://openalex.org/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.9940999746322632,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing 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":0.7235088348388672},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6540570855140686},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6085372567176819},{"id":"https://openalex.org/keywords/prior-probability","display_name":"Prior probability","score":0.5730751156806946},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.5691409111022949},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5596426129341125},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.5130473375320435},{"id":"https://openalex.org/keywords/transformation","display_name":"Transformation (genetics)","score":0.49636489152908325},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.4515945613384247},{"id":"https://openalex.org/keywords/boundary","display_name":"Boundary (topology)","score":0.4514821469783783},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.4320893883705139},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.41362473368644714},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3990102708339691},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.37711068987846375},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.2952868938446045},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.27583515644073486},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1591455042362213}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7235088348388672},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6540570855140686},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6085372567176819},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.5730751156806946},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.5691409111022949},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5596426129341125},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.5130473375320435},{"id":"https://openalex.org/C204241405","wikidata":"https://www.wikidata.org/wiki/Q461499","display_name":"Transformation (genetics)","level":3,"score":0.49636489152908325},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.4515945613384247},{"id":"https://openalex.org/C62354387","wikidata":"https://www.wikidata.org/wiki/Q875399","display_name":"Boundary (topology)","level":2,"score":0.4514821469783783},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.4320893883705139},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.41362473368644714},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3990102708339691},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.37711068987846375},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2952868938446045},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.27583515644073486},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1591455042362213},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.0},{"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/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icarm.2019.8833973","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icarm.2019.8833973","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE 4th International Conference on Advanced Robotics and Mechatronics (ICARM)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W1901129140","https://openalex.org/W1903029394","https://openalex.org/W2070724998","https://openalex.org/W2124386111","https://openalex.org/W2163605009","https://openalex.org/W2194775991","https://openalex.org/W2340897893","https://openalex.org/W2412782625","https://openalex.org/W2549139847","https://openalex.org/W2558580397","https://openalex.org/W2560023338","https://openalex.org/W2565639579","https://openalex.org/W2592939477","https://openalex.org/W2598666589","https://openalex.org/W2601564443","https://openalex.org/W2630837129","https://openalex.org/W2741237521","https://openalex.org/W2752782242","https://openalex.org/W2796419292","https://openalex.org/W2799166040","https://openalex.org/W2884822772","https://openalex.org/W2952147788","https://openalex.org/W2962891704","https://openalex.org/W2963420686","https://openalex.org/W2963727650","https://openalex.org/W2964309882","https://openalex.org/W6639824700","https://openalex.org/W6739696289"],"related_works":["https://openalex.org/W2580650124","https://openalex.org/W4386190339","https://openalex.org/W2968424575","https://openalex.org/W3142333283","https://openalex.org/W3122088529","https://openalex.org/W3041320102","https://openalex.org/W2111669074","https://openalex.org/W3209312100","https://openalex.org/W2964954556","https://openalex.org/W2890372105"],"abstract_inverted_index":{"Deep":[0],"residual":[1],"network":[2],"and":[3,70,95],"multi-scale":[4,23,57,138],"context":[5],"module":[6,94,103,142],"become":[7],"two":[8,85],"key":[9],"ingredients":[10],"in":[11,18,35,59],"recent":[12],"researches":[13],"which":[14,151],"made":[15],"great":[16],"progress":[17],"semantic":[19],"segmentation":[20,76,156],"tasks.":[21],"The":[22,101,117],"features":[24],"utilized":[25],"by":[26,114],"these":[27],"networks":[28],"are":[29,43],"also":[30,196],"proven":[31],"to":[32,46,50,68,131,146,198],"be":[33,121],"effective":[34],"refining":[36],"object":[37,162],"boundaries.":[38],"However,":[39],"regular":[40],"convolution":[41],"kernels":[42],"inherently":[44],"limited":[45],"geometric":[47,112],"transformation":[48],"due":[49],"the":[51,62,71,125,133,140,148,153,184,187,191],"fixed":[52],"structure.":[53],"Additionally,":[54],"when":[55,159],"integrating":[56],"information":[58,109,119],"boundary":[60,108],"detection,":[61],"capability":[63],"of":[64,73,127,135,164,173,190],"global":[65,144,149],"attention":[66],"starts":[67],"degrade":[69],"problem":[72,157],"inconsistent":[74,154],"intra-class":[75,155],"will":[77,120],"occur.":[78],"To":[79],"solve":[80],"above":[81],"problems,":[82],"we":[83],"propose":[84],"novel":[86],"architectures":[87],"dubbed":[88],"as":[89],"Deformable":[90],"Residual":[91],"Boundary":[92],"(DRB)":[93],"Dual":[96],"Global":[97],"Prior":[98],"(DGP)":[99],"module.":[100],"DRB":[102],"can":[104],"learn":[105],"more":[106],"refined":[107],"from":[110],"non-rigid":[111],"structures":[113],"deformable":[115],"convolutions.":[116],"extracted":[118],"further":[122],"embedded":[123],"into":[124],"branches":[126],"an":[128],"encoder-decoder":[129],"architecture":[130],"handle":[132],"deformation":[134],"objects.":[136],"For":[137],"fusion,":[139],"DGP":[141],"utilizes":[143],"priors":[145],"enhance":[147],"attention,":[150],"alleviates":[152],"especially":[158],"dealing":[160],"with":[161,183],"surfaces":[163],"different":[165],"textures/resolutions.":[166],"We":[167],"achieve":[168],"a":[169],"mean":[170],"IOU":[171],"value":[172],"80.3%":[174],"without":[175],"coarse":[176,185],"set,":[177,186],"outperforming":[178],"state-of-the-art":[179],"approaches.":[180,200],"When":[181],"training":[182],"final":[188],"mIOU":[189],"proposed":[192],"method":[193],"is":[194],"80.9%,":[195],"competitive":[197],"other":[199]},"counts_by_year":[{"year":2021,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}