{"id":"https://openalex.org/W3047725879","doi":"https://doi.org/10.1109/tgrs.2020.3016086","title":"Road Segmentation for Remote Sensing Images Using Adversarial Spatial Pyramid Networks","display_name":"Road Segmentation for Remote Sensing Images Using Adversarial Spatial Pyramid Networks","publication_year":2020,"publication_date":"2020-08-21","ids":{"openalex":"https://openalex.org/W3047725879","doi":"https://doi.org/10.1109/tgrs.2020.3016086","mag":"3047725879"},"language":"en","primary_location":{"id":"doi:10.1109/tgrs.2020.3016086","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2020.3016086","pdf_url":null,"source":{"id":"https://openalex.org/S111326731","display_name":"IEEE Transactions on Geoscience and Remote Sensing","issn_l":"0196-2892","issn":["0196-2892","1558-0644"],"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 Geoscience and Remote Sensing","raw_type":"journal-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2008.04021","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5079232252","display_name":"Pourya Shamsolmoali","orcid":"https://orcid.org/0000-0002-0263-1661"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Pourya Shamsolmoali","raw_affiliation_strings":["Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai, China"],"raw_orcid":"https://orcid.org/0000-0002-0263-1661","affiliations":[{"raw_affiliation_string":"Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai, China","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5009550171","display_name":"Masoumeh Zareapoor","orcid":"https://orcid.org/0000-0002-3991-0584"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Masoumeh Zareapoor","raw_affiliation_strings":["Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai, China"],"raw_orcid":"https://orcid.org/0000-0002-3991-0584","affiliations":[{"raw_affiliation_string":"Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai, China","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5066119228","display_name":"Huiyu Zhou","orcid":"https://orcid.org/0000-0003-1634-9840"},"institutions":[{"id":"https://openalex.org/I153648349","display_name":"University of Leicester","ror":"https://ror.org/04h699437","country_code":"GB","type":"education","lineage":["https://openalex.org/I153648349"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Huiyu Zhou","raw_affiliation_strings":["School of Informatics, University of Leicester, Leicester, U.K","[School of Informatics, University of Leicester, Leicester, U.K]"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Informatics, University of Leicester, Leicester, U.K","institution_ids":["https://openalex.org/I153648349"]},{"raw_affiliation_string":"[School of Informatics, University of Leicester, Leicester, U.K]","institution_ids":["https://openalex.org/I153648349"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100694558","display_name":"Ruili Wang","orcid":"https://orcid.org/0000-0001-7117-2772"},"institutions":[{"id":"https://openalex.org/I119273862","display_name":"Central South University of Forestry and Technology","ror":"https://ror.org/02czw2k81","country_code":"CN","type":"education","lineage":["https://openalex.org/I119273862"]},{"id":"https://openalex.org/I139660479","display_name":"Central South University","ror":"https://ror.org/00f1zfq44","country_code":"CN","type":"education","lineage":["https://openalex.org/I139660479"]},{"id":"https://openalex.org/I51158804","display_name":"Massey University","ror":"https://ror.org/052czxv31","country_code":"NZ","type":"education","lineage":["https://openalex.org/I51158804"]}],"countries":["CN","NZ"],"is_corresponding":false,"raw_author_name":"Ruili Wang","raw_affiliation_strings":["School of Logistics and Transportation, Central South University of Forestry and Technology, Changsha, China","School of Natural and Computational Sciences, Massey University, Auckland, New Zealand"],"raw_orcid":"https://orcid.org/0000-0001-7117-2772","affiliations":[{"raw_affiliation_string":"School of Logistics and Transportation, Central South University of Forestry and Technology, Changsha, China","institution_ids":["https://openalex.org/I119273862","https://openalex.org/I139660479"]},{"raw_affiliation_string":"School of Natural and Computational Sciences, Massey University, Auckland, New Zealand","institution_ids":["https://openalex.org/I51158804"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100404947","display_name":"Jie Yang","orcid":"https://orcid.org/0000-0003-4801-7162"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jie Yang","raw_affiliation_strings":["Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai, China","Shanghai Jiao Tong University"],"raw_orcid":"https://orcid.org/0000-0003-4801-7162","affiliations":[{"raw_affiliation_string":"Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai, China","institution_ids":["https://openalex.org/I183067930"]},{"raw_affiliation_string":"Shanghai Jiao Tong University","institution_ids":["https://openalex.org/I183067930"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":15.4644,"has_fulltext":false,"cited_by_count":145,"citation_normalized_percentile":{"value":0.99605311,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":100},"biblio":{"volume":"59","issue":"6","first_page":"4673","last_page":"4688"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13282","display_name":"Automated Road and Building Extraction","score":0.9995999932289124,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean Engineering"},"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/T13282","display_name":"Automated Road and Building Extraction","score":0.9995999932289124,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean 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/T11164","display_name":"Remote Sensing and LiDAR Applications","score":0.9940000176429749,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental 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":0.9603000283241272,"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":0.8520081043243408},{"id":"https://openalex.org/keywords/pyramid","display_name":"Pyramid (geometry)","score":0.7281637191772461},{"id":"https://openalex.org/keywords/discriminator","display_name":"Discriminator","score":0.6473226547241211},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6458394527435303},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6408450603485107},{"id":"https://openalex.org/keywords/flops","display_name":"FLOPS","score":0.5772011876106262},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5749998688697815},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4756973683834076},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.4618249535560608},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.46091464161872864},{"id":"https://openalex.org/keywords/generator","display_name":"Generator (circuit theory)","score":0.44436249136924744},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.43970200419425964},{"id":"https://openalex.org/keywords/network-architecture","display_name":"Network architecture","score":0.4384393095970154},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.33655354380607605}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8520081043243408},{"id":"https://openalex.org/C142575187","wikidata":"https://www.wikidata.org/wiki/Q3358290","display_name":"Pyramid (geometry)","level":2,"score":0.7281637191772461},{"id":"https://openalex.org/C2779803651","wikidata":"https://www.wikidata.org/wiki/Q5282088","display_name":"Discriminator","level":3,"score":0.6473226547241211},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6458394527435303},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6408450603485107},{"id":"https://openalex.org/C3826847","wikidata":"https://www.wikidata.org/wiki/Q188768","display_name":"FLOPS","level":2,"score":0.5772011876106262},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5749998688697815},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4756973683834076},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4618249535560608},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.46091464161872864},{"id":"https://openalex.org/C2780992000","wikidata":"https://www.wikidata.org/wiki/Q17016113","display_name":"Generator (circuit theory)","level":3,"score":0.44436249136924744},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.43970200419425964},{"id":"https://openalex.org/C193415008","wikidata":"https://www.wikidata.org/wiki/Q639681","display_name":"Network architecture","level":2,"score":0.4384393095970154},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.33655354380607605},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"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/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"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/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","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},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"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/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.0},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/tgrs.2020.3016086","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2020.3016086","pdf_url":null,"source":{"id":"https://openalex.org/S111326731","display_name":"IEEE Transactions on Geoscience and Remote Sensing","issn_l":"0196-2892","issn":["0196-2892","1558-0644"],"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 Geoscience and Remote Sensing","raw_type":"journal-article"},{"id":"pmh:oai:arXiv.org:2008.04021","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2008.04021","pdf_url":"https://arxiv.org/pdf/2008.04021","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"pmh:oai:figshare.com:article/12813827","is_oa":true,"landing_page_url":"https://figshare.com/articles/journal_contribution/Road_Segmentation_for_Remote_Sensing_Images_using_Adversarial_Spatial_Pyramid_Networks/12813827","pdf_url":null,"source":{"id":"https://openalex.org/S4306402621","display_name":"INDIGO (University of Illinois at Chicago)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I39422238","host_organization_name":"University of Illinois Chicago","host_organization_lineage":["https://openalex.org/I39422238"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2008.04021","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2008.04021","pdf_url":"https://arxiv.org/pdf/2008.04021","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[{"display_name":"Reduced inequalities","score":0.6200000047683716,"id":"https://metadata.un.org/sdg/10"}],"awards":[{"id":"https://openalex.org/G3816866674","display_name":null,"funder_award_id":"61806125","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8081038515","display_name":null,"funder_award_id":"61977046","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":70,"referenced_works":["https://openalex.org/W1498238238","https://openalex.org/W1522301498","https://openalex.org/W1686810756","https://openalex.org/W1731081199","https://openalex.org/W2012188213","https://openalex.org/W2109255472","https://openalex.org/W2121189958","https://openalex.org/W2121927366","https://openalex.org/W2131846894","https://openalex.org/W2139427051","https://openalex.org/W2141729166","https://openalex.org/W2194775991","https://openalex.org/W2340897893","https://openalex.org/W2342699585","https://openalex.org/W2548275288","https://openalex.org/W2565639579","https://openalex.org/W2593886839","https://openalex.org/W2604918751","https://openalex.org/W2620858446","https://openalex.org/W2752782242","https://openalex.org/W2785672818","https://openalex.org/W2786492053","https://openalex.org/W2798964604","https://openalex.org/W2886397424","https://openalex.org/W2888654602","https://openalex.org/W2888832231","https://openalex.org/W2898504016","https://openalex.org/W2899607431","https://openalex.org/W2900518108","https://openalex.org/W2910065734","https://openalex.org/W2914965007","https://openalex.org/W2921660688","https://openalex.org/W2927122915","https://openalex.org/W2939571759","https://openalex.org/W2942366787","https://openalex.org/W2950776302","https://openalex.org/W2953419979","https://openalex.org/W2957667237","https://openalex.org/W2962793481","https://openalex.org/W2962808524","https://openalex.org/W2963420686","https://openalex.org/W2963709863","https://openalex.org/W2963898168","https://openalex.org/W2963981733","https://openalex.org/W2963989027","https://openalex.org/W2964121744","https://openalex.org/W2964971217","https://openalex.org/W2965521953","https://openalex.org/W2967085153","https://openalex.org/W2968945589","https://openalex.org/W2979332630","https://openalex.org/W2998115938","https://openalex.org/W3007227736","https://openalex.org/W3024167159","https://openalex.org/W3102864715","https://openalex.org/W3151530552","https://openalex.org/W4239175444","https://openalex.org/W4297708031","https://openalex.org/W4301206121","https://openalex.org/W4309845474","https://openalex.org/W6631190155","https://openalex.org/W6637373629","https://openalex.org/W6637618735","https://openalex.org/W6729482032","https://openalex.org/W6735926176","https://openalex.org/W6748243251","https://openalex.org/W6754479648","https://openalex.org/W6757842588","https://openalex.org/W6765779288","https://openalex.org/W6778059799"],"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/W1608433645","https://openalex.org/W3131497135"],"abstract_inverted_index":{"Road":[0],"extraction":[1],"in":[2,205,247],"remote":[3],"sensing":[4],"images":[5,183],"is":[6,99,148,168],"of":[7,14,17,25,52,123,135,161,191,201,207],"great":[8],"importance":[9],"for":[10,69,138],"a":[11,33,61,78,116,144,171],"wide":[12,189],"range":[13,190],"applications.":[15],"Because":[16],"the":[18,26,89,92,107,121,124,133,136,153,159,162,166,178,181,185,198,202,220,240,248],"complex":[19],"background,":[20],"and":[21,39,73,94,106,157,184,209,227],"high":[22,50],"density,":[23],"most":[24],"existing":[27],"methods":[28],"fail":[29],"to":[30,64,87,101,110,150],"accurately":[31],"extract":[32],"road":[34,74],"network":[35,82,118,137],"that":[36,119],"appears":[37],"correct":[38],"complete.":[40],"Moreover,":[41],"they":[42],"suffer":[43],"from":[44,131,152],"either":[45],"insufficient":[46],"training":[47],"data":[48,195,222],"or":[49],"costs":[51],"manual":[53],"annotation.":[54],"To":[55],"address":[56],"these":[57],"problems,":[58],"we":[59],"introduce":[60],"new":[62],"model":[63,126,167,214],"apply":[65],"structured":[66],"domain":[67],"adaption":[68],"synthetic":[70,104],"image":[71],"generation":[72],"segmentation.":[75],"We":[76,113],"incorporate":[77],"feature":[79,155],"pyramid":[80],"(FP)":[81],"into":[83],"generative":[84],"adversarial":[85],"networks":[86],"minimize":[88],"difference":[90,179],"between":[91,180],"source":[93],"target":[95],"domains.":[96],"A":[97,188],"generator":[98],"learned":[100],"produce":[102],"quality":[103],"images,":[105],"discriminator":[108],"attempts":[109],"distinguish":[111],"them.":[112],"also":[114],"propose":[115],"FP":[117],"improves":[120],"performance":[122,200],"proposed":[125,203],"by":[127,170],"extracting":[128],"effective":[129],"features":[130],"all":[132],"layers":[134],"describing":[139],"different":[140],"scales'":[141],"objects.":[142],"Indeed,":[143],"novel":[145],"scale-wise":[146],"architecture":[147],"introduced":[149],"learn":[151],"multilevel":[154],"maps":[156],"improve":[158],"semantics":[160],"features.":[163],"For":[164],"optimization,":[165],"trained":[169],"joint":[172],"reconstruction":[173],"loss":[174],"function,":[175],"which":[176],"minimizes":[177],"fake":[182],"real":[186],"ones.":[187],"experiments":[192],"on":[193,219],"three":[194],"sets":[196],"prove":[197],"superior":[199],"approach":[204],"terms":[206],"accuracy":[208,236],"efficiency.":[210],"In":[211],"particular,":[212],"our":[213],"achieves":[215],"state-of-the-art":[216,244],"78.86":[217],"IOU":[218],"Massachusetts":[221],"set":[223],"with":[224,230],"14.89M":[225],"parameters":[226],"86.78B":[228],"FLOPs,":[229],"4\u00d7":[231],"fewer":[232],"FLOPs":[233],"but":[234],"higher":[235],"(+3.47%":[237],"IOU)":[238],"than":[239],"top":[241],"performer":[242],"among":[243],"approaches":[245],"used":[246],"evaluation.":[249]},"counts_by_year":[{"year":2026,"cited_by_count":7},{"year":2025,"cited_by_count":22},{"year":2024,"cited_by_count":29},{"year":2023,"cited_by_count":28},{"year":2022,"cited_by_count":34},{"year":2021,"cited_by_count":22},{"year":2020,"cited_by_count":3}],"updated_date":"2026-07-03T08:13:44.112507","created_date":"2025-10-10T00:00:00"}
