{"id":"https://openalex.org/W3136460283","doi":"https://doi.org/10.1109/tits.2021.3066401","title":"EPRNet: Efficient Pyramid Representation Network for Real-Time Street Scene Segmentation","display_name":"EPRNet: Efficient Pyramid Representation Network for Real-Time Street Scene Segmentation","publication_year":2021,"publication_date":"2021-03-23","ids":{"openalex":"https://openalex.org/W3136460283","doi":"https://doi.org/10.1109/tits.2021.3066401","mag":"3136460283"},"language":"en","primary_location":{"id":"doi:10.1109/tits.2021.3066401","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tits.2021.3066401","pdf_url":null,"source":{"id":"https://openalex.org/S144771191","display_name":"IEEE Transactions on Intelligent Transportation Systems","issn_l":"1524-9050","issn":["1524-9050","1558-0016"],"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 Intelligent Transportation Systems","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/A5033579560","display_name":"Quan Tang","orcid":"https://orcid.org/0000-0003-4011-6166"},"institutions":[{"id":"https://openalex.org/I90610280","display_name":"South China University of Technology","ror":"https://ror.org/0530pts50","country_code":"CN","type":"education","lineage":["https://openalex.org/I90610280"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Quan Tang","raw_affiliation_strings":["School of Computer Science and Engineering, South China University of Technology, Guangzhou, China"],"raw_orcid":"https://orcid.org/0000-0003-4011-6166","affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, South China University of Technology, Guangzhou, China","institution_ids":["https://openalex.org/I90610280"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5005463262","display_name":"Fagui Liu","orcid":"https://orcid.org/0000-0003-1135-4982"},"institutions":[{"id":"https://openalex.org/I90610280","display_name":"South China University of Technology","ror":"https://ror.org/0530pts50","country_code":"CN","type":"education","lineage":["https://openalex.org/I90610280"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fagui Liu","raw_affiliation_strings":["School of Computer Science and Engineering, South China University of Technology, Guangzhou, China"],"raw_orcid":"https://orcid.org/0000-0003-1135-4982","affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, South China University of Technology, Guangzhou, China","institution_ids":["https://openalex.org/I90610280"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5010421115","display_name":"Jun Jiang","orcid":"https://orcid.org/0000-0002-8406-994X"},"institutions":[{"id":"https://openalex.org/I90610280","display_name":"South China University of Technology","ror":"https://ror.org/0530pts50","country_code":"CN","type":"education","lineage":["https://openalex.org/I90610280"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jun Jiang","raw_affiliation_strings":["School of Computer Science and Engineering, South China University of Technology, Guangzhou, China"],"raw_orcid":"https://orcid.org/0000-0002-8406-994X","affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, South China University of Technology, Guangzhou, China","institution_ids":["https://openalex.org/I90610280"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100433417","display_name":"Yu Zhang","orcid":"https://orcid.org/0000-0001-8034-5305"},"institutions":[{"id":"https://openalex.org/I90610280","display_name":"South China University of Technology","ror":"https://ror.org/0530pts50","country_code":"CN","type":"education","lineage":["https://openalex.org/I90610280"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yu Zhang","raw_affiliation_strings":["School of Computer Science and Engineering, South China University of Technology, Guangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, South China University of Technology, Guangzhou, China","institution_ids":["https://openalex.org/I90610280"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I90610280"],"apc_list":null,"apc_paid":null,"fwci":0.848,"has_fulltext":false,"cited_by_count":14,"citation_normalized_percentile":{"value":0.74366169,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":"23","issue":"7","first_page":"7008","last_page":"7016"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9998000264167786,"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":0.9998000264167786,"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/T10331","display_name":"Video Surveillance and Tracking Methods","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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.998199999332428,"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/pyramid","display_name":"Pyramid (geometry)","score":0.7466859221458435},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7373793125152588},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6962641477584839},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6632607579231262},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5675958395004272},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5337183475494385},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5198103189468384},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.5099350214004517},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4910118877887726},{"id":"https://openalex.org/keywords/encoding","display_name":"Encoding (memory)","score":0.4368090033531189},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.4153737425804138},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.08978927135467529}],"concepts":[{"id":"https://openalex.org/C142575187","wikidata":"https://www.wikidata.org/wiki/Q3358290","display_name":"Pyramid (geometry)","level":2,"score":0.7466859221458435},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7373793125152588},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6962641477584839},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6632607579231262},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5675958395004272},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5337183475494385},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5198103189468384},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.5099350214004517},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4910118877887726},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.4368090033531189},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4153737425804138},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.08978927135467529},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tits.2021.3066401","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tits.2021.3066401","pdf_url":null,"source":{"id":"https://openalex.org/S144771191","display_name":"IEEE Transactions on Intelligent Transportation Systems","issn_l":"1524-9050","issn":["1524-9050","1558-0016"],"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 Intelligent Transportation Systems","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7099999785423279,"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":48,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W1677182931","https://openalex.org/W1686810756","https://openalex.org/W1817277359","https://openalex.org/W1901129140","https://openalex.org/W1903029394","https://openalex.org/W2097117768","https://openalex.org/W2117539524","https://openalex.org/W2171943915","https://openalex.org/W2186615578","https://openalex.org/W2194775991","https://openalex.org/W2340897893","https://openalex.org/W2412782625","https://openalex.org/W2531409750","https://openalex.org/W2560023338","https://openalex.org/W2565639579","https://openalex.org/W2592939477","https://openalex.org/W2630837129","https://openalex.org/W2762439315","https://openalex.org/W2799213142","https://openalex.org/W2809446072","https://openalex.org/W2883406652","https://openalex.org/W2886934227","https://openalex.org/W2901189993","https://openalex.org/W2963418739","https://openalex.org/W2963419596","https://openalex.org/W2963840672","https://openalex.org/W2963881378","https://openalex.org/W2963890956","https://openalex.org/W2964217532","https://openalex.org/W2969808474","https://openalex.org/W2971198903","https://openalex.org/W2985276900","https://openalex.org/W3011933327","https://openalex.org/W3199526541","https://openalex.org/W4293406525","https://openalex.org/W4297775537","https://openalex.org/W6622964371","https://openalex.org/W6637373629","https://openalex.org/W6638480814","https://openalex.org/W6686509673","https://openalex.org/W6696085341","https://openalex.org/W6717372056","https://openalex.org/W6737664043","https://openalex.org/W6739696289","https://openalex.org/W6756319154","https://openalex.org/W6757855356","https://openalex.org/W6771640722"],"related_works":["https://openalex.org/W2965546495","https://openalex.org/W4389116644","https://openalex.org/W2153315159","https://openalex.org/W3103844505","https://openalex.org/W259157601","https://openalex.org/W4205463238","https://openalex.org/W2761785940","https://openalex.org/W2110523656","https://openalex.org/W2797752778","https://openalex.org/W1522196789"],"abstract_inverted_index":{"Current":[0],"scene":[1],"segmentation":[2,39],"methods":[3,48],"suffer":[4],"from":[5],"cumbersome":[6],"model":[7,41],"structures":[8],"and":[9,43,78,88,107,121,128,138,154,163],"high":[10],"computational":[11],"complexity,":[12],"impeding":[13],"their":[14],"applications":[15],"to":[16,75,118],"real-world":[17],"scenarios":[18],"that":[19,94],"require":[20],"real-time":[21],"processing.":[22],"This":[23],"paper":[24],"proposes":[25],"a":[26,112,145,182],"novel":[27],"Efficient":[28],"Pyramid":[29,114],"Representation":[30,115],"Network":[31],"(EPRNet),":[32],"which":[33],"strikes":[34],"an":[35,85],"innovative":[36],"record":[37],"on":[38,53,152,171],"accuracy,":[40],"lightness":[42],"inference":[44],"efficiency.":[45],"Unlike":[46],"existing":[47],"delivering":[49],"transfer":[50],"learning":[51],"based":[52],"pixel":[54],"features":[55,97],"of":[56,101,141,184],"limited":[57],"receptive":[58,80],"fields":[59],"encoded":[60],"by":[61],"shallow":[62,127],"image":[63],"classification":[64],"backbones,":[65],"EPRNet":[66,134,167],"distributes":[67],"multi-scale":[68,96,142],"representations":[69,140],"throughout":[70],"the":[71,158,172],"feature":[72],"encoding":[73],"flow":[74],"quickly":[76],"enlarge":[77],"enrich":[79],"fields.":[81],"Specifically,":[82],"we":[83,110],"introduce":[84],"extremely":[86],"lightweight":[87],"efficient":[89],"Multi-scale":[90],"Processing":[91],"Unit":[92],"(MPU)":[93],"encodes":[95],"through":[98],"parallel":[99],"convolutions":[100],"different":[102],"kernels.":[103],"By":[104],"combining":[105],"MPU":[106],"residual":[108],"learning,":[109],"propose":[111],"core":[113],"Module":[116],"(PRM)":[117],"correctly":[119],"acquire":[120],"aggregate":[122],"region-based":[123],"contexts":[124],"in":[125],"both":[126],"deep":[129],"layers.":[130],"In":[131],"this":[132],"way,":[133],"can":[135],"encode":[136],"discriminative":[137],"comprehensive":[139],"objects":[143],"with":[144,176],"compact":[146],"structure.":[147],"We":[148],"conduct":[149],"extensive":[150],"experiments":[151],"Cityscapes":[153,173],"CamVid":[155],"datasets,":[156],"demonstrating":[157],"superiority.":[159],"Without":[160],"any":[161],"extra":[162],"coarse":[164],"labeled":[165],"data,":[166],"obtains":[168],"mIoU":[169],"73.9%":[170],"test":[174],"set":[175],"only":[177],"0.9":[178],"million":[179],"parameters":[180],"at":[181],"speed":[183],"42":[185],"FPS.":[186]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":2},{"year":2022,"cited_by_count":7}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}
