{"id":"https://openalex.org/W4385366257","doi":"https://doi.org/10.1109/iv55152.2023.10186532","title":"Transfer Learning for Driver Pose Estimation from Synthetic Data","display_name":"Transfer Learning for Driver Pose Estimation from Synthetic Data","publication_year":2023,"publication_date":"2023-06-04","ids":{"openalex":"https://openalex.org/W4385366257","doi":"https://doi.org/10.1109/iv55152.2023.10186532"},"language":"en","primary_location":{"id":"doi:10.1109/iv55152.2023.10186532","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iv55152.2023.10186532","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 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/A5092565011","display_name":"Daniel Sagmeister","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Daniel Sagmeister","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058722055","display_name":"Dominik Sch\u00f6rkhuber","orcid":"https://orcid.org/0000-0003-2015-6507"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dominik Sch\u00f6rkhuber","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5070974614","display_name":"Matej Nezveda","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Matej Nezveda","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006986947","display_name":"Fabian Stiedl","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fabian Stiedl","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5025970873","display_name":"Maria Schimkowitsch","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Maria Schimkowitsch","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5026627076","display_name":"Margrit Gelautz","orcid":"https://orcid.org/0000-0002-9476-0865"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Margrit Gelautz","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.6069,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.75965677,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"7"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10812","display_name":"Human Pose and Action Recognition","score":0.9993000030517578,"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/T10812","display_name":"Human Pose and Action Recognition","score":0.9993000030517578,"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":0.9983000159263611,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9915000200271606,"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":0.8322757482528687},{"id":"https://openalex.org/keywords/synthetic-data","display_name":"Synthetic data","score":0.7898368835449219},{"id":"https://openalex.org/keywords/transfer-of-learning","display_name":"Transfer of learning","score":0.6961871385574341},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6689513921737671},{"id":"https://openalex.org/keywords/pose","display_name":"Pose","score":0.6351282596588135},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.6039701700210571},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.5945197343826294},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.4919257164001465},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.44980138540267944},{"id":"https://openalex.org/keywords/labeled-data","display_name":"Labeled data","score":0.4134880304336548},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.34268903732299805},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3239961862564087},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.10723474621772766}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8322757482528687},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.7898368835449219},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.6961871385574341},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6689513921737671},{"id":"https://openalex.org/C52102323","wikidata":"https://www.wikidata.org/wiki/Q1671968","display_name":"Pose","level":2,"score":0.6351282596588135},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.6039701700210571},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.5945197343826294},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.4919257164001465},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.44980138540267944},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.4134880304336548},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.34268903732299805},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3239961862564087},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.10723474621772766},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iv55152.2023.10186532","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iv55152.2023.10186532","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE Intelligent Vehicles Symposium (IV)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":38,"referenced_works":["https://openalex.org/W1861492603","https://openalex.org/W2108598243","https://openalex.org/W2576289912","https://openalex.org/W2738749209","https://openalex.org/W2744999500","https://openalex.org/W2791017519","https://openalex.org/W2952122856","https://openalex.org/W2963794138","https://openalex.org/W2964238416","https://openalex.org/W2971166028","https://openalex.org/W2982335979","https://openalex.org/W2986674040","https://openalex.org/W2991727786","https://openalex.org/W3000322757","https://openalex.org/W3010244393","https://openalex.org/W3011872542","https://openalex.org/W3014641072","https://openalex.org/W3024582231","https://openalex.org/W3041133507","https://openalex.org/W3045471054","https://openalex.org/W3080348805","https://openalex.org/W3118281159","https://openalex.org/W3128640141","https://openalex.org/W3135909330","https://openalex.org/W3162938128","https://openalex.org/W3181599570","https://openalex.org/W3202732536","https://openalex.org/W3205583421","https://openalex.org/W3208404748","https://openalex.org/W3208820243","https://openalex.org/W3208980919","https://openalex.org/W4226285067","https://openalex.org/W4288287657","https://openalex.org/W4362554711","https://openalex.org/W6767133472","https://openalex.org/W6791734417","https://openalex.org/W6799013692","https://openalex.org/W6810865246"],"related_works":["https://openalex.org/W2130553454","https://openalex.org/W3022007134","https://openalex.org/W4317548404","https://openalex.org/W2087783760","https://openalex.org/W4312659495","https://openalex.org/W1509924131","https://openalex.org/W4385366257","https://openalex.org/W3101007570","https://openalex.org/W4387910575","https://openalex.org/W3163689946"],"abstract_inverted_index":{"The":[0,100,141],"training":[1,98,133],"of":[2,13,48,61,67,76,94,102,119,131],"computer":[3],"vision":[4],"models":[5],"for":[6,45,85,158],"human":[7],"pose":[8,20,79],"estimation":[9],"requires":[10],"large":[11,46,92],"amounts":[12,47,93],"data.":[14,99],"Since":[15],"labelling":[16],"image":[17],"data":[18,37,50,63,87,106,127,134],"with":[19,123,147],"keypoints":[21],"is":[22,107,139,156],"very":[23],"time":[24],"consuming":[25],"and":[26,40],"costly,":[27],"we":[28,57],"aim":[29],"to":[30,89],"alleviate":[31],"this":[32,55],"requirement":[33],"by":[34,121,149],"using":[35],"synthetic":[36,62,86,105,126],"during":[38,52],"pre-training":[39,122,146],"thus":[41],"relax":[42],"the":[43,59,65,74,103,136,152],"need":[44],"real":[49,132],"samples":[51],"fine-tuning.":[53,159],"To":[54],"end,":[56],"investigate":[58],"impact":[60],"on":[64],"performance":[66,117],"a":[68,115],"2D":[69],"keypoint":[70],"detection":[71],"model":[72],"in":[73,109],"context":[75],"driver":[77],"body":[78],"estimation.":[80],"We":[81,113],"present":[82],"our":[83,124],"approach":[84,143],"generation":[88],"automatically":[90],"provide":[91],"in-cabin":[95,125],"views":[96],"as":[97],"utilization":[101],"generated":[104],"evaluated":[108],"different":[110],"learning":[111],"schemes.":[112],"achieve":[114],"notable":[116],"gain":[118],"+30.5%":[120],"when":[128,151],"only":[129],"1%":[130],"from":[135],"DriPE":[137,154],"dataset":[138,155],"available.":[140],"proposed":[142],"also":[144],"outperforms":[145],"PeopleSansPeople":[148],"+8.3%":[150],"reduced":[153],"used":[157]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}