{"id":"https://openalex.org/W3173695934","doi":"https://doi.org/10.1109/iv48863.2021.9575247","title":"Spatio-Temporal Consistency for Semi-supervised Learning Using 3D Radar Cubes","display_name":"Spatio-Temporal Consistency for Semi-supervised Learning Using 3D Radar Cubes","publication_year":2021,"publication_date":"2021-07-11","ids":{"openalex":"https://openalex.org/W3173695934","doi":"https://doi.org/10.1109/iv48863.2021.9575247","mag":"3173695934"},"language":"en","primary_location":{"id":"doi:10.1109/iv48863.2021.9575247","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iv48863.2021.9575247","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 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/A5088203319","display_name":"Wei\u2010Yu Lee","orcid":"https://orcid.org/0000-0001-5552-6446"},"institutions":[{"id":"https://openalex.org/I32597200","display_name":"Ghent University","ror":"https://ror.org/00cv9y106","country_code":"BE","type":"education","lineage":["https://openalex.org/I32597200"]}],"countries":["BE"],"is_corresponding":false,"raw_author_name":"Wei-Yu Lee","raw_affiliation_strings":["TELIN-IPI, Ghent University-imec, Gent, Belgium"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"TELIN-IPI, Ghent University-imec, Gent, Belgium","institution_ids":["https://openalex.org/I32597200"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5057761165","display_name":"Martin Dimitrievski","orcid":"https://orcid.org/0000-0003-4477-7746"},"institutions":[{"id":"https://openalex.org/I32597200","display_name":"Ghent University","ror":"https://ror.org/00cv9y106","country_code":"BE","type":"education","lineage":["https://openalex.org/I32597200"]}],"countries":["BE"],"is_corresponding":false,"raw_author_name":"Martin Dimitrievski","raw_affiliation_strings":["TELIN-IPI, Ghent University-imec, Gent, Belgium"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"TELIN-IPI, Ghent University-imec, Gent, Belgium","institution_ids":["https://openalex.org/I32597200"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5028474463","display_name":"Ljubomir Jovanov","orcid":"https://orcid.org/0000-0001-8790-1116"},"institutions":[{"id":"https://openalex.org/I32597200","display_name":"Ghent University","ror":"https://ror.org/00cv9y106","country_code":"BE","type":"education","lineage":["https://openalex.org/I32597200"]}],"countries":["BE"],"is_corresponding":false,"raw_author_name":"Ljubomir Jovanov","raw_affiliation_strings":["TELIN-IPI, Ghent University-imec, Gent, Belgium"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"TELIN-IPI, Ghent University-imec, Gent, Belgium","institution_ids":["https://openalex.org/I32597200"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5071483672","display_name":"Wilfried Philips","orcid":"https://orcid.org/0000-0003-4456-4353"},"institutions":[{"id":"https://openalex.org/I32597200","display_name":"Ghent University","ror":"https://ror.org/00cv9y106","country_code":"BE","type":"education","lineage":["https://openalex.org/I32597200"]}],"countries":["BE"],"is_corresponding":false,"raw_author_name":"Wilfried Philips","raw_affiliation_strings":["TELIN-IPI, Ghent University-imec, Gent, Belgium"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"TELIN-IPI, Ghent University-imec, Gent, Belgium","institution_ids":["https://openalex.org/I32597200"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I32597200"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"785","last_page":"790"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11609","display_name":"Geophysical Methods and Applications","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"}},"topics":[{"id":"https://openalex.org/T11609","display_name":"Geophysical Methods and Applications","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/T11038","display_name":"Advanced SAR Imaging Techniques","score":0.9986000061035156,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace 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/T11698","display_name":"Underwater Acoustics Research","score":0.9954000115394592,"subfield":{"id":"https://openalex.org/subfields/1910","display_name":"Oceanography"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7771145701408386},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6804907321929932},{"id":"https://openalex.org/keywords/radar","display_name":"Radar","score":0.6794456839561462},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.5529277920722961},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5515303015708923},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.4548455476760864},{"id":"https://openalex.org/keywords/rgb-color-model","display_name":"RGB color model","score":0.43581265211105347},{"id":"https://openalex.org/keywords/radar-imaging","display_name":"Radar imaging","score":0.435048371553421},{"id":"https://openalex.org/keywords/timeline","display_name":"Timeline","score":0.42765742540359497},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.40861502289772034},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.40239235758781433},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.37728095054626465},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.340891569852829},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.11976030468940735}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7771145701408386},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6804907321929932},{"id":"https://openalex.org/C554190296","wikidata":"https://www.wikidata.org/wiki/Q47528","display_name":"Radar","level":2,"score":0.6794456839561462},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.5529277920722961},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5515303015708923},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.4548455476760864},{"id":"https://openalex.org/C82990744","wikidata":"https://www.wikidata.org/wiki/Q166194","display_name":"RGB color model","level":2,"score":0.43581265211105347},{"id":"https://openalex.org/C10929652","wikidata":"https://www.wikidata.org/wiki/Q7279985","display_name":"Radar imaging","level":3,"score":0.435048371553421},{"id":"https://openalex.org/C4438859","wikidata":"https://www.wikidata.org/wiki/Q186117","display_name":"Timeline","level":2,"score":0.42765742540359497},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.40861502289772034},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.40239235758781433},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.37728095054626465},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.340891569852829},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.11976030468940735},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","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}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/iv48863.2021.9575247","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iv48863.2021.9575247","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE Intelligent Vehicles Symposium (IV)","raw_type":"proceedings-article"},{"id":"pmh:oai:archive.ugent.be:8713966","is_oa":false,"landing_page_url":"https://biblio.ugent.be/publication/8713966","pdf_url":null,"source":{"id":"https://openalex.org/S4306400478","display_name":"Ghent University Academic Bibliography (Ghent University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I32597200","host_organization_name":"Ghent University","host_organization_lineage":["https://openalex.org/I32597200"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"ISBN: 9781728153940","raw_type":"info:eu-repo/semantics/conferenceObject"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320327336","display_name":"Vlaamse regering","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":34,"referenced_works":["https://openalex.org/W1901129140","https://openalex.org/W1903029394","https://openalex.org/W2530816535","https://openalex.org/W2592691248","https://openalex.org/W2883429621","https://openalex.org/W2927438889","https://openalex.org/W2951970475","https://openalex.org/W2953070460","https://openalex.org/W2964159205","https://openalex.org/W2964700958","https://openalex.org/W2969150035","https://openalex.org/W2970235454","https://openalex.org/W2976252039","https://openalex.org/W2995042771","https://openalex.org/W3009192276","https://openalex.org/W3010503765","https://openalex.org/W3016143006","https://openalex.org/W3020905020","https://openalex.org/W3035574168","https://openalex.org/W3036962712","https://openalex.org/W3090139470","https://openalex.org/W3094212830","https://openalex.org/W3105891528","https://openalex.org/W3120351402","https://openalex.org/W3161421216","https://openalex.org/W6639824700","https://openalex.org/W6733814495","https://openalex.org/W6760782946","https://openalex.org/W6764051988","https://openalex.org/W6767026726","https://openalex.org/W6771922894","https://openalex.org/W6774800982","https://openalex.org/W6774923209","https://openalex.org/W6784639621"],"related_works":["https://openalex.org/W1858249912","https://openalex.org/W2114034199","https://openalex.org/W4391249598","https://openalex.org/W2317428717","https://openalex.org/W2734259032","https://openalex.org/W3094038556","https://openalex.org/W2014772881","https://openalex.org/W4254228154","https://openalex.org/W4313855562","https://openalex.org/W2091422131"],"abstract_inverted_index":{"Radar":[0],"has":[1],"been":[2],"employed":[3],"as":[4],"a":[5,56],"key":[6],"component":[7],"of":[8,41,73,144,155],"perception":[9],"modules":[10],"for":[11,33],"more":[12,30],"than":[13,32],"two":[14],"decades.":[15],"However,":[16],"radar":[17,52,75,84],"image":[18],"labeling":[19],"requires":[20],"expert":[21],"knowledge.":[22],"At":[23],"the":[24,47,64,71,82,92,96,100,112,119,138,142,145,153,156],"same":[25],"time,":[26],"it":[27],"is":[28],"much":[29],"time-consuming":[31],"general":[34],"RGB":[35],"images,":[36],"which":[37],"impedes":[38],"further":[39],"developments":[40],"radar.":[42],"In":[43],"order":[44],"to":[45,77,90,94,137],"alleviate":[46],"high-cost":[48],"annotation":[49],"problem":[50],"in":[51],"datasets,":[53],"we":[54,69],"present":[55],"novel,":[57],"semi-supervised":[58],"deep":[59],"learning":[60],"method":[61,121],"based":[62],"on":[63,106,122],"spatio-temporal":[65],"consistency.":[66],"This":[67],"way":[68],"explore":[70],"potential":[72],"unlabeled":[74],"frames":[76,85],"enhance":[78],"performance.":[79],"We":[80,117],"utilize":[81],"consecutive":[83],"from":[86],"different":[87],"timeline":[88],"directions":[89],"encourage":[91],"model":[93],"learn":[95],"target":[97],"motion.":[98],"Moreover,":[99],"proposed":[101,120,146,157],"self-weighted":[102],"mechanism":[103],"avoids":[104],"over-fitting":[105],"certain":[107],"predominant":[108],"targets,":[109],"by":[110],"exploiting":[111],"supervised":[113],"classification":[114],"loss":[115],"dynamically.":[116],"evaluate":[118],"semantic":[123],"segmentation":[124],"and":[125,140],"Vulnerable":[126],"Road":[127],"Users":[128],"(VRUs)":[129],"detection":[130],"problems.":[131],"The":[132,148],"quantitative":[133],"results":[134],"compare":[135],"favourably":[136],"state-of-the-art":[139],"demonstrate":[141],"effectiveness":[143,154],"concepts.":[147],"ablation":[149],"studies":[150],"also":[151],"show":[152],"components.":[158]},"counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":4}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
