{"id":"https://openalex.org/W4408713055","doi":"https://doi.org/10.1109/itsc58415.2024.10920195","title":"Leveraging Bayesian Optimization to Enhance Self-Supervised Federated Learning of Monocular Depth Estimators","display_name":"Leveraging Bayesian Optimization to Enhance Self-Supervised Federated Learning of Monocular Depth Estimators","publication_year":2024,"publication_date":"2024-09-24","ids":{"openalex":"https://openalex.org/W4408713055","doi":"https://doi.org/10.1109/itsc58415.2024.10920195"},"language":"en","primary_location":{"id":"doi:10.1109/itsc58415.2024.10920195","is_oa":false,"landing_page_url":"https://doi.org/10.1109/itsc58415.2024.10920195","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC)","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/A5010653528","display_name":"Elton Soares","orcid":"https://orcid.org/0000-0002-7371-8035"},"institutions":[{"id":"https://openalex.org/I83648350","display_name":"Universidade Federal do Estado do Rio de Janeiro","ror":"https://ror.org/04tec8z30","country_code":"BR","type":"education","lineage":["https://openalex.org/I83648350"]}],"countries":["BR"],"is_corresponding":true,"raw_author_name":"Elton F. De S. Soares","raw_affiliation_strings":["IBM Research, and the Federal University of State of Rio de Janeiro (UNIRIO),Brazilian Research Lab (BRL),Rio de Janeiro,Brazil"],"affiliations":[{"raw_affiliation_string":"IBM Research, and the Federal University of State of Rio de Janeiro (UNIRIO),Brazilian Research Lab (BRL),Rio de Janeiro,Brazil","institution_ids":["https://openalex.org/I83648350"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5077314929","display_name":"Em\u00edlio Vital Brazil","orcid":"https://orcid.org/0000-0003-4982-3836"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Emilio Vital Brazil","raw_affiliation_strings":["BRL"],"affiliations":[{"raw_affiliation_string":"BRL","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5087835697","display_name":"Carlos Alberto V. Campos","orcid":"https://orcid.org/0000-0002-9979-6541"},"institutions":[{"id":"https://openalex.org/I83648350","display_name":"Universidade Federal do Estado do Rio de Janeiro","ror":"https://ror.org/04tec8z30","country_code":"BR","type":"education","lineage":["https://openalex.org/I83648350"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Carlos Alberto V. Campos","raw_affiliation_strings":["UNIRIO"],"affiliations":[{"raw_affiliation_string":"UNIRIO","institution_ids":["https://openalex.org/I83648350"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5010653528"],"corresponding_institution_ids":["https://openalex.org/I83648350"],"apc_list":null,"apc_paid":null,"fwci":0.2632,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.59725717,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"2420","last_page":"2425"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9990000128746033,"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":0.9990000128746033,"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":0.9983999729156494,"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"}},{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.996399998664856,"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":0.7884626388549805},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.6712378263473511},{"id":"https://openalex.org/keywords/monocular","display_name":"Monocular","score":0.6090582609176636},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.5837204456329346},{"id":"https://openalex.org/keywords/bayesian-optimization","display_name":"Bayesian optimization","score":0.5629155039787292},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5502814650535583},{"id":"https://openalex.org/keywords/federated-learning","display_name":"Federated learning","score":0.5010101795196533},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.45586657524108887},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.14258944988250732},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.09059751033782959}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7884626388549805},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.6712378263473511},{"id":"https://openalex.org/C65909025","wikidata":"https://www.wikidata.org/wiki/Q1945033","display_name":"Monocular","level":2,"score":0.6090582609176636},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5837204456329346},{"id":"https://openalex.org/C2778049539","wikidata":"https://www.wikidata.org/wiki/Q17002908","display_name":"Bayesian optimization","level":2,"score":0.5629155039787292},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5502814650535583},{"id":"https://openalex.org/C2992525071","wikidata":"https://www.wikidata.org/wiki/Q50818671","display_name":"Federated learning","level":2,"score":0.5010101795196533},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.45586657524108887},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.14258944988250732},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.09059751033782959}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/itsc58415.2024.10920195","is_oa":false,"landing_page_url":"https://doi.org/10.1109/itsc58415.2024.10920195","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC)","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":35,"referenced_works":["https://openalex.org/W2108598243","https://openalex.org/W2488789499","https://openalex.org/W2559655401","https://openalex.org/W2604272474","https://openalex.org/W2609883120","https://openalex.org/W2963316641","https://openalex.org/W2964968086","https://openalex.org/W2966437693","https://openalex.org/W2990138404","https://openalex.org/W3023371261","https://openalex.org/W3023574229","https://openalex.org/W3026182744","https://openalex.org/W3093708494","https://openalex.org/W3118635606","https://openalex.org/W3171189420","https://openalex.org/W3193756050","https://openalex.org/W3204874618","https://openalex.org/W4200495456","https://openalex.org/W4229029907","https://openalex.org/W4307079499","https://openalex.org/W4309679361","https://openalex.org/W4312572946","https://openalex.org/W4361761348","https://openalex.org/W4367189808","https://openalex.org/W4387415226","https://openalex.org/W4387559458","https://openalex.org/W4400351057","https://openalex.org/W4406267741","https://openalex.org/W6678911119","https://openalex.org/W6685261749","https://openalex.org/W6719982585","https://openalex.org/W6728757088","https://openalex.org/W6767088534","https://openalex.org/W6784106160","https://openalex.org/W6811110894"],"related_works":["https://openalex.org/W4298221930","https://openalex.org/W3106461837","https://openalex.org/W4287880334","https://openalex.org/W2777914285","https://openalex.org/W4378677776","https://openalex.org/W4366700029","https://openalex.org/W4285230481","https://openalex.org/W3013363440","https://openalex.org/W4287823391","https://openalex.org/W4401858220"],"abstract_inverted_index":{"Current":[0],"investigations":[1,105],"in":[2,157],"computer":[3],"vision":[4],"on":[5,12,40,164,195],"intelligent":[6],"transportation":[7],"systems":[8],"have":[9,32],"notably":[10],"centered":[11],"depth":[13,31,144],"estimation":[14,71],"from":[15],"images,":[16],"primarily":[17],"because":[18],"of":[19,25,81,109,171,179],"its":[20],"cost-efficiency":[21],"and":[22,72,98,138,149,183],"diverse":[23],"range":[24],"applications.":[26],"Monocular":[27],"methods":[28],"for":[29,77],"estimating":[30],"garnered":[33],"significant":[34],"interest":[35],"due":[36],"to":[37,49,116,141,152,181],"their":[38],"dependence":[39],"a":[41,175],"single":[42],"camera,":[43],"which":[44],"provides":[45],"considerable":[46],"versatility":[47],"compared":[48,151],"binocular":[50],"techniques":[51,64],"necessitating":[52],"two":[53],"fixed":[54],"cameras.":[55],"Despite":[56],"leveraging":[57],"sophisticated":[58],"self-supervised":[59,158],"deep":[60,139],"neural":[61],"network":[62,93],"learning":[63,112],"with":[65,113,146,191],"surrogate":[66],"tasks":[67],"such":[68],"as":[69],"pose":[70],"semantic":[73],"segmentation,":[74],"essential":[75],"requirements":[76],"the":[78,107,122,153,165,169,188],"practical":[79],"deployment":[80],"autonomous":[82,196],"vehicles":[83],"are":[84],"often":[85],"neglected.":[86],"These":[87],"entail":[88],"safeguarding":[89],"data":[90],"privacy,":[91],"minimizing":[92],"usage,":[94],"distributing":[95],"computational":[96,185],"expenses,":[97],"fortifying":[99],"resilience":[100],"against":[101],"connectivity":[102],"challenges.":[103],"Recent":[104],"underscore":[106],"efficacy":[108],"combining":[110],"federated":[111,136,159],"Bayesian":[114,134],"optimization":[115],"meet":[117],"these":[118],"criteria":[119],"without":[120],"compromising":[121],"model's":[123],"effectiveness.":[124],"Therefore,":[125],"we":[126],"present":[127],"BOFedSCDepth,":[128],"an":[129],"innovative":[130],"approach":[131],"that":[132],"combines":[133],"optimization,":[135],"learning,":[137],"self-supervision":[140],"train":[142],"monocular":[143],"estimators":[145],"improved":[147],"effectiveness":[148],"efficiency":[150],"current":[154],"state-of-the-art":[155],"method":[156],"learning.":[160],"Evaluation":[161],"experiments":[162],"conducted":[163],"KITTI":[166],"dataset":[167],"showcase":[168],"superiority":[170],"our":[172],"method,":[173],"achieving":[174],"communication":[176],"cost":[177],"reduction":[178],"up":[180],"33.3%":[182],"linear":[184],"costs":[186],"at":[187],"central":[189],"server":[190],"no":[192],"extra":[193],"burden":[194],"vehicles.":[197]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2025-12-28T23:10:05.387466","created_date":"2025-10-10T00:00:00"}