{"id":"https://openalex.org/W3118771842","doi":"https://doi.org/10.1109/iv47402.2020.9304836","title":"Real-Time Panoptic Segmentation with Prototype Masks for Automated Driving","display_name":"Real-Time Panoptic Segmentation with Prototype Masks for Automated Driving","publication_year":2020,"publication_date":"2020-10-19","ids":{"openalex":"https://openalex.org/W3118771842","doi":"https://doi.org/10.1109/iv47402.2020.9304836","mag":"3118771842"},"language":"en","primary_location":{"id":"doi:10.1109/iv47402.2020.9304836","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iv47402.2020.9304836","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE Intelligent Vehicles Symposium (IV)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5077794784","display_name":"Andra Petrovai","orcid":"https://orcid.org/0000-0002-4036-6336"},"institutions":[{"id":"https://openalex.org/I158333966","display_name":"Technical University of Cluj-Napoca","ror":"https://ror.org/03r8nwp71","country_code":"RO","type":"education","lineage":["https://openalex.org/I158333966"]}],"countries":["RO"],"is_corresponding":false,"raw_author_name":"Andra Petrovai","raw_affiliation_strings":["Technical University of Cluj-Napoca, Cluj-Napoca, Romania"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Technical University of Cluj-Napoca, Cluj-Napoca, Romania","institution_ids":["https://openalex.org/I158333966"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5047954457","display_name":"Sergiu Nedevschi","orcid":"https://orcid.org/0000-0003-2018-4647"},"institutions":[{"id":"https://openalex.org/I158333966","display_name":"Technical University of Cluj-Napoca","ror":"https://ror.org/03r8nwp71","country_code":"RO","type":"education","lineage":["https://openalex.org/I158333966"]}],"countries":["RO"],"is_corresponding":false,"raw_author_name":"Sergiu Nedevschi","raw_affiliation_strings":["Technical University of Cluj-Napoca, Cluj-Napoca, Romania"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Technical University of Cluj-Napoca, Cluj-Napoca, Romania","institution_ids":["https://openalex.org/I158333966"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I158333966"],"apc_list":null,"apc_paid":null,"fwci":0.6834,"has_fulltext":false,"cited_by_count":16,"citation_normalized_percentile":{"value":0.78508969,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1400","last_page":"1406"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9995999932289124,"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":0.9983999729156494,"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":0.8192936182022095},{"id":"https://openalex.org/keywords/panopticon","display_name":"Panopticon","score":0.75525963306427},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.7314324378967285},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7283348441123962},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.6085484027862549},{"id":"https://openalex.org/keywords/pyramid","display_name":"Pyramid (geometry)","score":0.6074666976928711},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5744302272796631},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5365713834762573},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.48619216680526733},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.4601190388202667},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4084668755531311},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.06977635622024536}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8192936182022095},{"id":"https://openalex.org/C138569888","wikidata":"https://www.wikidata.org/wiki/Q828310","display_name":"Panopticon","level":3,"score":0.75525963306427},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7314324378967285},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7283348441123962},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6085484027862549},{"id":"https://openalex.org/C142575187","wikidata":"https://www.wikidata.org/wiki/Q3358290","display_name":"Pyramid (geometry)","level":2,"score":0.6074666976928711},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5744302272796631},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5365713834762573},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.48619216680526733},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.4601190388202667},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4084668755531311},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.06977635622024536},{"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/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"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/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iv47402.2020.9304836","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iv47402.2020.9304836","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE Intelligent Vehicles Symposium (IV)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","score":0.8399999737739563,"display_name":"Sustainable cities and communities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":40,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W1861492603","https://openalex.org/W2194775991","https://openalex.org/W2340897893","https://openalex.org/W2557889580","https://openalex.org/W2558156561","https://openalex.org/W2565639579","https://openalex.org/W2613718673","https://openalex.org/W2781228439","https://openalex.org/W2809110088","https://openalex.org/W2902499724","https://openalex.org/W2904397670","https://openalex.org/W2910628332","https://openalex.org/W2911831070","https://openalex.org/W2962773068","https://openalex.org/W2963091558","https://openalex.org/W2963150697","https://openalex.org/W2963350373","https://openalex.org/W2963446712","https://openalex.org/W2963677766","https://openalex.org/W2963775509","https://openalex.org/W2963857746","https://openalex.org/W2964241181","https://openalex.org/W2965182628","https://openalex.org/W2981537222","https://openalex.org/W2982161360","https://openalex.org/W2982770724","https://openalex.org/W2988473009","https://openalex.org/W2990217526","https://openalex.org/W2991405684","https://openalex.org/W2993182889","https://openalex.org/W2999219213","https://openalex.org/W3004359336","https://openalex.org/W3034512672","https://openalex.org/W3035049382","https://openalex.org/W3035295311","https://openalex.org/W3035709993","https://openalex.org/W4288029642","https://openalex.org/W6639102338","https://openalex.org/W6738279954"],"related_works":["https://openalex.org/W2921107741","https://openalex.org/W2197002326","https://openalex.org/W4233922521","https://openalex.org/W3204968380","https://openalex.org/W1710116222","https://openalex.org/W2800383628","https://openalex.org/W2494728058","https://openalex.org/W2345320341","https://openalex.org/W4381850983","https://openalex.org/W3205665319"],"abstract_inverted_index":{"In":[0],"this":[1],"paper":[2],"we":[3,69],"propose":[4],"a":[5,35,57,92],"fast":[6],"fully":[7],"convolutional":[8],"neural":[9],"network":[10,55,128],"for":[11,42,48,64,111],"panoptic":[12,32,85,126],"segmentation":[13,33,127],"that":[14,73,124],"can":[15],"provide":[16],"an":[17],"accurate":[18],"semantic":[19],"and":[20,39,60,76,114,131,136],"instance-level":[21],"representation":[22],"of":[23,51],"the":[24,27,84,120],"environment":[25],"in":[26,99,101],"2D":[28],"space.":[29],"We":[30],"tackle":[31],"as":[34,45,47],"dense":[36],"classification":[37],"problem":[38],"generate":[40],"masks":[41,90],"stuff":[43],"classes":[44],"well":[46],"each":[49],"instance":[50],"things":[52],"classes.":[53],"Our":[54,96],"employs":[56],"shared":[58],"backbone":[59],"Feature":[61],"Pyramid":[62],"Network":[63],"multi-scale":[65],"feature":[66],"extraction":[67],"which":[68],"extend":[70],"with":[71,133],"dual-decoders":[72],"learn":[74],"background":[75],"foreground":[77],"specific":[78],"masks.":[79],"Guided":[80],"by":[81],"object":[82],"proposals,":[83],"head":[86],"assembles":[87],"location-sensitive":[88],"prototype":[89],"using":[91],"learned":[93],"weighting":[94],"scheme.":[95],"solution":[97],"runs":[98],"real-time,":[100],"82":[102],"ms":[103],"on":[104,119],"high":[105],"resolution":[106],"images,":[107],"making":[108],"it":[109],"suitable":[110],"robotic":[112],"applications":[113],"automated":[115],"driving.":[116],"Extensive":[117],"experiments":[118],"Cityscapes":[121],"dataset":[122],"demonstrate":[123],"our":[125],"is":[129],"robust":[130],"accurate,":[132],"57.3%":[134],"PQ":[135],"76.9%":[137],"mIoU.":[138]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":3}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
