{"id":"https://openalex.org/W3136055522","doi":"https://doi.org/10.1109/imcom51814.2021.9377401","title":"TSS-Net: Time-based Semantic Segmentation Neural Network for Road Scene Understanding","display_name":"TSS-Net: Time-based Semantic Segmentation Neural Network for Road Scene Understanding","publication_year":2021,"publication_date":"2021-01-04","ids":{"openalex":"https://openalex.org/W3136055522","doi":"https://doi.org/10.1109/imcom51814.2021.9377401","mag":"3136055522"},"language":"en","primary_location":{"id":"doi:10.1109/imcom51814.2021.9377401","is_oa":false,"landing_page_url":"https://doi.org/10.1109/imcom51814.2021.9377401","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 15th International Conference on Ubiquitous Information Management and Communication (IMCOM)","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/A5112245885","display_name":"Tin Trung Duong","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Tin Trung Duong","raw_affiliation_strings":["College of Information and Communication Engineering, SKKU, Suwon, Korea"],"affiliations":[{"raw_affiliation_string":"College of Information and Communication Engineering, SKKU, Suwon, Korea","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5083470396","display_name":"Huy-Hung Nguyen","orcid":"https://orcid.org/0000-0001-5394-9381"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Huy-Hung Nguyen","raw_affiliation_strings":["College of Information and Communication Engineering, SKKU, Suwon, Korea"],"affiliations":[{"raw_affiliation_string":"College of Information and Communication Engineering, SKKU, Suwon, Korea","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5024137527","display_name":"Jae Wook Jeon","orcid":"https://orcid.org/0000-0003-0037-112X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jae Wook Jeon","raw_affiliation_strings":["College of Information and Communication Engineering, SKKU, Suwon, Korea"],"affiliations":[{"raw_affiliation_string":"College of Information and Communication Engineering, SKKU, Suwon, Korea","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5112245885"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.1921,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.45812092,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"abs 1712 6080","issue":null,"first_page":"1","last_page":"7"},"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/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.9976999759674072,"subfield":{"id":"https://openalex.org/subfields/2203","display_name":"Automotive 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/T11606","display_name":"Infrastructure Maintenance and Monitoring","score":0.9947999715805054,"subfield":{"id":"https://openalex.org/subfields/2205","display_name":"Civil and Structural Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8310993909835815},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.7791587114334106},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7695172429084778},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6512573957443237},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.5964512825012207},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5565109252929688},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.5468258261680603},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5285177230834961},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5043960809707642},{"id":"https://openalex.org/keywords/scale-space-segmentation","display_name":"Scale-space segmentation","score":0.4842626750469208},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4685424566268921},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.42823994159698486},{"id":"https://openalex.org/keywords/segmentation-based-object-categorization","display_name":"Segmentation-based object categorization","score":0.42010629177093506},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.23577755689620972}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8310993909835815},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7791587114334106},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7695172429084778},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6512573957443237},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.5964512825012207},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5565109252929688},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.5468258261680603},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5285177230834961},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5043960809707642},{"id":"https://openalex.org/C65885262","wikidata":"https://www.wikidata.org/wiki/Q7429708","display_name":"Scale-space segmentation","level":4,"score":0.4842626750469208},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4685424566268921},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.42823994159698486},{"id":"https://openalex.org/C25694479","wikidata":"https://www.wikidata.org/wiki/Q7446278","display_name":"Segmentation-based object categorization","level":5,"score":0.42010629177093506},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.23577755689620972},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/imcom51814.2021.9377401","is_oa":false,"landing_page_url":"https://doi.org/10.1109/imcom51814.2021.9377401","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 15th International Conference on Ubiquitous Information Management and Communication (IMCOM)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","score":0.5,"display_name":"Sustainable cities and communities"}],"awards":[{"id":"https://openalex.org/G1074828277","display_name":null,"funder_award_id":"2020R1A2C3011286","funder_id":"https://openalex.org/F4320322120","funder_display_name":"National Research Foundation of Korea"}],"funders":[{"id":"https://openalex.org/F4320322120","display_name":"National Research Foundation of Korea","ror":"https://ror.org/013aysd81"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":34,"referenced_works":["https://openalex.org/W1686810756","https://openalex.org/W1901129140","https://openalex.org/W1903029394","https://openalex.org/W2108598243","https://openalex.org/W2163605009","https://openalex.org/W2167222293","https://openalex.org/W2193145675","https://openalex.org/W2194775991","https://openalex.org/W2338908902","https://openalex.org/W2340897893","https://openalex.org/W2402144811","https://openalex.org/W2531409750","https://openalex.org/W2562137921","https://openalex.org/W2607839767","https://openalex.org/W2780740184","https://openalex.org/W2799352588","https://openalex.org/W2913292019","https://openalex.org/W2953384591","https://openalex.org/W2962835968","https://openalex.org/W2963037989","https://openalex.org/W2963446712","https://openalex.org/W2963611454","https://openalex.org/W2963881378","https://openalex.org/W2964309882","https://openalex.org/W3034971973","https://openalex.org/W3106250896","https://openalex.org/W3132455321","https://openalex.org/W6637373629","https://openalex.org/W6684191040","https://openalex.org/W6687483927","https://openalex.org/W6703652217","https://openalex.org/W6713134421","https://openalex.org/W6747394537","https://openalex.org/W6750749703"],"related_works":["https://openalex.org/W3144569342","https://openalex.org/W2185902295","https://openalex.org/W2945274617","https://openalex.org/W2103507220","https://openalex.org/W2055202857","https://openalex.org/W2371519352","https://openalex.org/W4205800335","https://openalex.org/W2386644571","https://openalex.org/W2372421320","https://openalex.org/W2901890255"],"abstract_inverted_index":{"In":[0],"this":[1,92,127,187],"research,":[2],"a":[3,51,94,97,114],"multitask":[4],"convolutional":[5],"neural":[6],"network":[7,35,43],"that":[8],"can":[9,132,163,190],"do":[10],"end-to-end":[11],"road":[12,61],"scene":[13,52,62,89,105],"classification":[14,175,195],"and":[15,54,75],"semantic":[16,40,122],"segmentation,":[17],"which":[18,37,69],"are":[19,81,111],"the":[20,34,64,78,84,88,103,107,120,134,137,142,150,152,174,182,192],"two":[21,55],"crucial":[22],"tasks":[23],"for":[24,73],"advanced":[25],"driver":[26],"assistance":[27],"systems":[28],"(ADAS),":[29],"is":[30],"proposed.":[31],"We":[32],"name":[33],"TSS":[36],"means":[38],"time-based":[39,56],"segmentation.":[41],"The":[42],"contains":[44],"three":[45],"main":[46],"modules:":[47],"an":[48],"image":[49,67,79,184],"encoder,":[50],"classifier,":[53],"segmentation":[57,116,123,138,169,201],"decoders.":[58,76],"For":[59],"each":[60],"image,":[63],"encoder":[65],"extracts":[66],"features":[68,80,110],"will":[70],"be":[71,164],"used":[72],"classifier":[74,85],"Next,":[77],"fed":[82,112],"to":[83,86,113,118,196],"predict":[87],"type":[90],"(in":[91],"case":[93],"day":[95],"or":[96],"night":[98],"scene).":[99],"Then,":[100],"based":[101],"on":[102,171],"predicted":[104],"type,":[106],"same":[108,183],"extracted":[109],"corresponding":[115],"decoder":[117,129],"produce":[119],"final":[121],"result.":[124],"By":[125],"using":[126],"classification-driven":[128],"approach,":[130,188],"we":[131,189],"improve":[133],"accuracy":[135,199],"of":[136,154,173,179],"model,":[139],"even":[140],"when":[141],"model":[143],"has":[144,158],"been":[145,159],"trained":[146],"excessively":[147],"earlier.":[148],"Through":[149],"experiment,":[151],"validity":[153],"our":[155],"proposed":[156],"method":[157],"proven.":[160],"Our":[161],"approach":[162],"considered":[165],"as":[166],"stacking":[167],"multiple":[168],"modules":[170],"top":[172],"module":[176],"with":[177],"all":[178],"them":[180],"share":[181],"encoder.":[185],"With":[186],"utilize":[191],"result":[193],"from":[194],"gain":[197],"more":[198],"in":[200,202],"one":[203],"feed":[204],"forward":[205],"only.":[206]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2021,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
