{"id":"https://openalex.org/W3015379147","doi":"https://doi.org/10.1109/access.2020.2987080","title":"Efficient Fast Semantic Segmentation Using Continuous Shuffle Dilated Convolutions","display_name":"Efficient Fast Semantic Segmentation Using Continuous Shuffle Dilated Convolutions","publication_year":2020,"publication_date":"2020-01-01","ids":{"openalex":"https://openalex.org/W3015379147","doi":"https://doi.org/10.1109/access.2020.2987080","mag":"3015379147"},"language":"en","primary_location":{"id":"doi:10.1109/access.2020.2987080","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2020.2987080","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8948470/09063469.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/6287639/8948470/09063469.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5074765098","display_name":"Xuegang Hu","orcid":"https://orcid.org/0000-0002-9169-9827"},"institutions":[{"id":"https://openalex.org/I10535382","display_name":"Chongqing University of Posts and Telecommunications","ror":"https://ror.org/03dgaqz26","country_code":"CN","type":"education","lineage":["https://openalex.org/I10535382"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xuegang Hu","raw_affiliation_strings":["School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing, China","institution_ids":["https://openalex.org/I10535382"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5102773315","display_name":"Haibo Wang","orcid":"https://orcid.org/0000-0001-7866-4171"},"institutions":[{"id":"https://openalex.org/I10535382","display_name":"Chongqing University of Posts and Telecommunications","ror":"https://ror.org/03dgaqz26","country_code":"CN","type":"education","lineage":["https://openalex.org/I10535382"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haibo Wang","raw_affiliation_strings":["Laboratory of Intelligent Analysis and Decision on Complex Systems, Chongqing University of Posts and Telecommunications, Chongqing, China"],"raw_orcid":"https://orcid.org/0000-0001-7866-4171","affiliations":[{"raw_affiliation_string":"Laboratory of Intelligent Analysis and Decision on Complex Systems, Chongqing University of Posts and Telecommunications, Chongqing, China","institution_ids":["https://openalex.org/I10535382"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I10535382"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":1.8208,"has_fulltext":true,"cited_by_count":25,"citation_normalized_percentile":{"value":0.87489122,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":"8","issue":null,"first_page":"70913","last_page":"70924"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":1.0,"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":1.0,"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":0.9990000128746033,"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":0.9986000061035156,"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/computer-science","display_name":"Computer science","score":0.832785964012146},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.7534900307655334},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.6768597960472107},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5774334669113159},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5467719435691833},{"id":"https://openalex.org/keywords/intersection","display_name":"Intersection (aeronautics)","score":0.5304688215255737},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.49872779846191406},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.49342218041419983},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.415638267993927},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4049607813358307},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3402738571166992},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.07541638612747192}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.832785964012146},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7534900307655334},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.6768597960472107},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5774334669113159},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5467719435691833},{"id":"https://openalex.org/C64543145","wikidata":"https://www.wikidata.org/wiki/Q162942","display_name":"Intersection (aeronautics)","level":2,"score":0.5304688215255737},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.49872779846191406},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.49342218041419983},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.415638267993927},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4049607813358307},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3402738571166992},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.07541638612747192},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0},{"id":"https://openalex.org/C146978453","wikidata":"https://www.wikidata.org/wiki/Q3798668","display_name":"Aerospace engineering","level":1,"score":0.0},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2020.2987080","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2020.2987080","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8948470/09063469.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:796307b65a014c049276815d02d9c918","is_oa":true,"landing_page_url":"https://doaj.org/article/796307b65a014c049276815d02d9c918","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","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":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 8, Pp 70913-70924 (2020)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2020.2987080","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2020.2987080","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8948470/09063469.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.49000000953674316,"id":"https://metadata.un.org/sdg/8","display_name":"Decent work and economic growth"}],"awards":[{"id":"https://openalex.org/G5870243763","display_name":null,"funder_award_id":"cstc2017jcyjBX0037","funder_id":"https://openalex.org/F4320323172","funder_display_name":"Natural Science Foundation of Chongqing"}],"funders":[{"id":"https://openalex.org/F4320323172","display_name":"Natural Science Foundation of Chongqing","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3015379147.pdf","grobid_xml":"https://content.openalex.org/works/W3015379147.grobid-xml"},"referenced_works_count":46,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W1522301498","https://openalex.org/W1677182931","https://openalex.org/W1686810756","https://openalex.org/W1745334888","https://openalex.org/W1836465849","https://openalex.org/W1901129140","https://openalex.org/W1903029394","https://openalex.org/W2171943915","https://openalex.org/W2183341477","https://openalex.org/W2194775991","https://openalex.org/W2279098554","https://openalex.org/W2340897893","https://openalex.org/W2400000673","https://openalex.org/W2412782625","https://openalex.org/W2560023338","https://openalex.org/W2618530766","https://openalex.org/W2762439315","https://openalex.org/W2809446072","https://openalex.org/W2883780447","https://openalex.org/W2886934227","https://openalex.org/W2888270240","https://openalex.org/W2895340641","https://openalex.org/W2901189993","https://openalex.org/W2907965334","https://openalex.org/W2955058313","https://openalex.org/W2962912109","https://openalex.org/W2963125010","https://openalex.org/W2963418739","https://openalex.org/W2963840672","https://openalex.org/W2963881378","https://openalex.org/W2964217532","https://openalex.org/W2981524957","https://openalex.org/W2981689412","https://openalex.org/W4293406525","https://openalex.org/W4297775537","https://openalex.org/W6631190155","https://openalex.org/W6637373629","https://openalex.org/W6638667902","https://openalex.org/W6639824700","https://openalex.org/W6695314431","https://openalex.org/W6696085341","https://openalex.org/W6717372056","https://openalex.org/W6737664043","https://openalex.org/W6756319154","https://openalex.org/W6757855356"],"related_works":["https://openalex.org/W2348909947","https://openalex.org/W4292672442","https://openalex.org/W2362101859","https://openalex.org/W2791431590","https://openalex.org/W2941610985","https://openalex.org/W4235810826","https://openalex.org/W3000105423","https://openalex.org/W2350688482","https://openalex.org/W2890372105","https://openalex.org/W1522196789"],"abstract_inverted_index":{"It":[0],"is":[1,36,46,55,64,91,127],"difficult":[2],"for":[3,66],"many":[4],"semantic":[5,20,88],"segmentation":[6,21,89,161],"methods":[7],"to":[8,129],"perform":[9],"useful":[10],"inferences":[11],"under":[12],"extremely":[13],"resource-constrained":[14],"devices;":[15],"therefore,":[16],"an":[17],"efficient":[18,160],"fast":[19,87],"network":[22,90,158],"(EFSNet)":[23],"that":[24,102],"employs":[25],"a":[26,33,61,86,134,146],"continuous":[27],"shuffle":[28],"dilated":[29,58],"convolution":[30],"(CSDC)":[31],"and":[32,51,69,79,98,122,142],"up-sampling":[34,62,80],"module":[35,63,78],"proposed":[37,65,113],"in":[38,131],"this":[39],"paper.":[40],"First,":[41],"the":[42,52,75,82,96,103,112],"number":[43],"of":[44,136],"parameters":[45,119],"reduced":[47],"by":[48,57,111],"group":[49],"convolutions":[50],"receptive":[53],"field":[54],"enlarged":[56],"convolution.":[59],"Second,":[60],"reducing":[67],"noise":[68],"increasing":[70],"inference":[71],"speed.":[72],"Finally,":[73],"with":[74,115,153,162],"above":[76],"CSDC":[77],"module,":[81],"EFSNet":[83,114],"based":[84],"on":[85,95,145],"obtained.":[92],"Our":[93],"experiments":[94],"CamVid":[97],"Cityscapes":[99],"datasets":[100],"show":[101],"mean":[104],"intersection":[105],"over":[106],"union":[107],"(mIoU)":[108],"values":[109],"obtained":[110],"only":[116],"173":[117],"k":[118],"are":[120],"61.1%":[121],"61.9%,":[123],"respectively.":[124],"The":[125],"method":[126],"able":[128],"run":[130],"real-time":[132],"at":[133],"speed":[135],"332":[137],"frames":[138],"per":[139],"second":[140],"(FPS)":[141],"107":[143],"FPS":[144],"single":[147],"NVIDIA":[148],"Titan":[149],"Xp":[150],"GPU.":[151],"Compared":[152],"several":[154],"existing":[155],"methods,":[156],"our":[157],"achieves":[159],"low":[163],"resource":[164],"consumption.":[165]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":8},{"year":2021,"cited_by_count":5},{"year":2020,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
