{"id":"https://openalex.org/W4385624340","doi":"https://doi.org/10.1109/icufn57995.2023.10199608","title":"MPNet: Multiscale predictions based on feature pyramid network for semantic segmentation","display_name":"MPNet: Multiscale predictions based on feature pyramid network for semantic segmentation","publication_year":2023,"publication_date":"2023-07-04","ids":{"openalex":"https://openalex.org/W4385624340","doi":"https://doi.org/10.1109/icufn57995.2023.10199608"},"language":"en","primary_location":{"id":"doi:10.1109/icufn57995.2023.10199608","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/icufn57995.2023.10199608","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 Fourteenth International Conference on Ubiquitous and Future Networks (ICUFN)","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/A5043792148","display_name":"Van Toan Quyen","orcid":null},"institutions":[{"id":"https://openalex.org/I31419693","display_name":"Kyungpook National University","ror":"https://ror.org/040c17130","country_code":"KR","type":"education","lineage":["https://openalex.org/I31419693"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Van Toan Quyen","raw_affiliation_strings":["Kyungpook National University,Electronic and Electrical Engineering,Daegu,South of Korea","Electronic and Electrical Engineering, Kyungpook National University, Daegu, South of Korea"],"affiliations":[{"raw_affiliation_string":"Kyungpook National University,Electronic and Electrical Engineering,Daegu,South of Korea","institution_ids":["https://openalex.org/I31419693"]},{"raw_affiliation_string":"Electronic and Electrical Engineering, Kyungpook National University, Daegu, South of Korea","institution_ids":["https://openalex.org/I31419693"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100447997","display_name":"Min Young Kim","orcid":"https://orcid.org/0000-0001-7263-3403"},"institutions":[{"id":"https://openalex.org/I31419693","display_name":"Kyungpook National University","ror":"https://ror.org/040c17130","country_code":"KR","type":"education","lineage":["https://openalex.org/I31419693"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Min Young Kim","raw_affiliation_strings":["Kyungpook National University,Electronic and Electrical Engineering,Daegu,South of Korea","Electronic and Electrical Engineering, Kyungpook National University, Daegu, South of Korea"],"affiliations":[{"raw_affiliation_string":"Kyungpook National University,Electronic and Electrical Engineering,Daegu,South of Korea","institution_ids":["https://openalex.org/I31419693"]},{"raw_affiliation_string":"Electronic and Electrical Engineering, Kyungpook National University, Daegu, South of Korea","institution_ids":["https://openalex.org/I31419693"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5043792148"],"corresponding_institution_ids":["https://openalex.org/I31419693"],"apc_list":null,"apc_paid":null,"fwci":0.246,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.51636335,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"114","last_page":"119"},"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/T13282","display_name":"Automated Road and Building Extraction","score":0.9991000294685364,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9987999796867371,"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":0.7903059720993042},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7774488925933838},{"id":"https://openalex.org/keywords/pooling","display_name":"Pooling","score":0.7286338210105896},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.7277810573577881},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6841657161712646},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6561807990074158},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5019998550415039},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.4882228672504425},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.447984516620636},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.4432297348976135},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.42547351121902466},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4228597581386566},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.4198282063007355},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.37525564432144165},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.09587550163269043}],"concepts":[{"id":"https://openalex.org/C142575187","wikidata":"https://www.wikidata.org/wiki/Q3358290","display_name":"Pyramid (geometry)","level":2,"score":0.7903059720993042},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7774488925933838},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.7286338210105896},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.7277810573577881},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6841657161712646},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6561807990074158},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5019998550415039},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4882228672504425},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.447984516620636},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.4432297348976135},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.42547351121902466},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4228597581386566},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.4198282063007355},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.37525564432144165},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.09587550163269043},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","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/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","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/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"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/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icufn57995.2023.10199608","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/icufn57995.2023.10199608","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 Fourteenth International Conference on Ubiquitous and Future Networks (ICUFN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7799999713897705,"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320311649","display_name":"Ministry of Education","ror":"https://ror.org/036nq5137"},{"id":"https://openalex.org/F4320320671","display_name":"National Research Foundation","ror":"https://ror.org/05s0g1g46"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W1686810756","https://openalex.org/W1903029394","https://openalex.org/W2194775991","https://openalex.org/W2775208825","https://openalex.org/W2787091153","https://openalex.org/W2800452261","https://openalex.org/W2813013735","https://openalex.org/W2921526792","https://openalex.org/W2960251737","https://openalex.org/W2962862396","https://openalex.org/W2963418739","https://openalex.org/W2963419596","https://openalex.org/W2964217532","https://openalex.org/W2965380104","https://openalex.org/W3005226210","https://openalex.org/W3008156909","https://openalex.org/W3082097505","https://openalex.org/W3108450508","https://openalex.org/W3118753716","https://openalex.org/W3190437803","https://openalex.org/W3194686611","https://openalex.org/W4246193833","https://openalex.org/W6637373629","https://openalex.org/W6746954591","https://openalex.org/W6760613829","https://openalex.org/W6766273390"],"related_works":["https://openalex.org/W2953234277","https://openalex.org/W2626256601","https://openalex.org/W2022849497","https://openalex.org/W3081299480","https://openalex.org/W2407190427","https://openalex.org/W2919210741","https://openalex.org/W2907584218","https://openalex.org/W3002446410","https://openalex.org/W4390224712","https://openalex.org/W4322096758"],"abstract_inverted_index":{"Semantic":[0],"segmentation":[1,199],"is":[2,54,158,172,187,211],"a":[3,15,27,59,214],"complex":[4],"topic":[5],"where":[6],"they":[7],"assign":[8],"each":[9,151,170],"pixel":[10],"of":[11,42,62,71,143,150,155,169,216],"an":[12,130],"image":[13],"with":[14,117],"corresponding":[16],"class":[17],"and":[18,91,105,120,178,234],"demand":[19],"accuracy":[20],"at":[21,229],"objective":[22],"boundaries.":[23],"The":[24,79,99,184,198,209],"method":[25],"plays":[26],"vital":[28],"role":[29],"in":[30,57],"scene-understanding":[31],"scenarios.":[32],"For":[33],"self-driving":[34],"applications,":[35],"the":[36,77,83,87,92,96,115,122,147,163,175,179,190],"input":[37],"source":[38],"includes":[39],"various":[40],"types":[41],"objects":[43],"such":[44],"as":[45,174],"trucks,":[46],"people,":[47],"or":[48],"traffic":[49],"signs.":[50],"One":[51],"receptive":[52],"field":[53],"only":[55],"effective":[56,131],"capturing":[58],"short":[60],"range":[61],"sizes.":[63],"Feature":[64],"pyramid":[65,142,193],"network":[66,134],"(FPN)":[67],"utilizes":[68],"different":[69],"fields":[70],"view":[72],"to":[73,136,161,195,203],"extract":[74],"information":[75,85,94],"from":[76,86,95,206],"input.":[78],"FPN":[80],"approach":[81],"obtains":[82],"spatial":[84,192],"high-resolution":[88],"feature":[89,101,168],"map":[90],"semantic":[93,123],"lower":[97],"scales.":[98],"final":[100],"representation":[102],"contains":[103],"coarse":[104],"fine":[106],"details,":[107],"but":[108],"it":[109],"has":[110],"some":[111],"drawbacks.":[112],"They":[113],"burden":[114],"system":[116],"extensive":[118],"computation":[119],"reduce":[121],"information.":[124,183],"In":[125],"this":[126],"paper,":[127],"we":[128],"devise":[129],"multiscale":[132,141],"predictions":[133,144],"(MPNet)":[135],"address":[137],"these":[138],"issues.":[139],"A":[140,153,166],"effectively":[145],"processes":[146],"prominent":[148],"characteristics":[149],"feature.":[152],"pair":[154],"adjacent":[156],"features":[157],"combined":[159],"together":[160],"predict":[162],"output":[164],"separately.":[165],"lower-scale":[167],"prediction":[171],"assigned":[173],"contextual":[176,185],"contributor,":[177],"other":[180],"provides":[181],"coarser":[182],"branch":[186],"passed":[188],"through":[189],"atrous":[191],"pooling":[194],"improve":[196],"performance.":[197],"scores":[200],"are":[201],"fused":[202],"obtain":[204],"advantages":[205],"all":[207],"predictions.":[208],"model":[210],"validated":[212],"by":[213],"series":[215],"experiments":[217],"on":[218,232,237],"open":[219],"data":[220],"sets.":[221],"We":[222],"have":[223],"achieved":[224],"good":[225],"results":[226],"76.5%":[227],"mIoU":[228,236],"50":[230],"FPS":[231],"Cityscapes":[233],"43.9%":[235],"Mapillary":[238],"Vistas.":[239]},"counts_by_year":[{"year":2024,"cited_by_count":2}],"updated_date":"2025-12-21T01:58:51.020947","created_date":"2025-10-10T00:00:00"}