{"id":"https://openalex.org/W4391125516","doi":"https://doi.org/10.48550/arxiv.2401.10397","title":"Analyzing and Mitigating Bias for Vulnerable Classes: Towards Balanced Representation in Dataset","display_name":"Analyzing and Mitigating Bias for Vulnerable Classes: Towards Balanced Representation in Dataset","publication_year":2024,"publication_date":"2024-01-18","ids":{"openalex":"https://openalex.org/W4391125516","doi":"https://doi.org/10.48550/arxiv.2401.10397"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2401.10397","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2401.10397","pdf_url":"https://arxiv.org/pdf/2401.10397","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2401.10397","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5059315576","display_name":"Dewant Katare","orcid":"https://orcid.org/0000-0002-3412-7603"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Katare, Dewant","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5070108326","display_name":"David Nogu\u00e9ro","orcid":"https://orcid.org/0000-0001-6979-9330"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Noguero, David Solans","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5010641593","display_name":"Souneil Park","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Park, Souneil","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049671186","display_name":"Nicolas Kourtellis","orcid":"https://orcid.org/0000-0002-5674-1698"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kourtellis, Nicolas","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5062073470","display_name":"Marijn Janssen","orcid":"https://orcid.org/0000-0001-6211-8790"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Janssen, Marijn","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5063593427","display_name":"Aaron Yi Ding","orcid":"https://orcid.org/0000-0003-4173-031X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ding, Aaron Yi","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5059315576"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9890000224113464,"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":0.9890000224113464,"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/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9703999757766724,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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":0.9678000211715698,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6928352117538452},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5957877039909363},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.5665614604949951},{"id":"https://openalex.org/keywords/resampling","display_name":"Resampling","score":0.5646893382072449},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5290701985359192},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.45943185687065125},{"id":"https://openalex.org/keywords/performance-metric","display_name":"Performance metric","score":0.4579533338546753},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.439566433429718},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.43743225932121277},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.41315987706184387},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.1000598669052124},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.07811996340751648},{"id":"https://openalex.org/keywords/political-science","display_name":"Political science","score":0.0703517496585846}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6928352117538452},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5957877039909363},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.5665614604949951},{"id":"https://openalex.org/C150921843","wikidata":"https://www.wikidata.org/wiki/Q1170431","display_name":"Resampling","level":2,"score":0.5646893382072449},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5290701985359192},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.45943185687065125},{"id":"https://openalex.org/C2780898871","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Performance metric","level":2,"score":0.4579533338546753},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.439566433429718},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.43743225932121277},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.41315987706184387},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.1000598669052124},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.07811996340751648},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0703517496585846},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"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/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2401.10397","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2401.10397","pdf_url":"https://arxiv.org/pdf/2401.10397","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2401.10397","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2401.10397","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2401.10397","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2401.10397","pdf_url":"https://arxiv.org/pdf/2401.10397","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4391125516.pdf"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4361804730","https://openalex.org/W2142113611","https://openalex.org/W2334467465","https://openalex.org/W2018387840","https://openalex.org/W2087870008","https://openalex.org/W2045629210","https://openalex.org/W2162534555","https://openalex.org/W2752178021","https://openalex.org/W2143024819","https://openalex.org/W4247159817"],"abstract_inverted_index":{"The":[0],"accuracy":[1],"and":[2,21,51,81,89,114,120,127,139,146,162,169],"fairness":[3],"of":[4,42],"perception":[5],"systems":[6],"in":[7,27,45,137,160,186],"autonomous":[8],"driving":[9,29],"are":[10,56],"essential,":[11],"especially":[12],"for":[13,103,117,150,155,183],"vulnerable":[14,69],"road":[15,70],"users":[16],"such":[17],"as":[18],"cyclists,":[19],"pedestrians,":[20],"motorcyclists":[22],"who":[23],"face":[24],"significant":[25],"risks":[26],"urban":[28],"environments.":[30],"While":[31],"mainstream":[32],"research":[33,60,174],"primarily":[34],"enhances":[35],"class":[36,49,66,77],"performance":[37,98],"metrics,":[38],"the":[39,46,54,94,130,151],"hidden":[40],"traits":[41],"bias":[43,83,121],"inheritance":[44],"AI":[47],"models,":[48],"imbalances":[50,67],"disparities":[52,102],"within":[53],"datasets":[55],"often":[57],"overlooked.":[58],"Our":[59],"addresses":[61],"these":[62],"issues":[63],"by":[64],"investigating":[65],"among":[68],"users,":[71],"with":[72,93],"a":[73],"focus":[74,111],"on":[75,112],"analyzing":[76],"distribution,":[78],"evaluating":[79],"performance,":[80],"assessing":[82],"impact.":[84],"Utilizing":[85],"popular":[86],"CNN":[87,152],"models":[88,179],"Vision":[90],"Transformers":[91],"(ViTs)":[92],"nuScenes":[95],"dataset,":[96],"our":[97],"evaluation":[99],"indicates":[100],"detection":[101],"underrepresented":[104],"classes.":[105],"Compared":[106],"to":[107,144,148,167,171,176],"related":[108],"work,":[109],"we":[110,134,157],"metric-specific":[113],"Cost-Sensitive":[115],"learning":[116],"model":[118],"optimization":[119],"mitigation,":[122],"which":[123],"includes":[124],"data":[125],"augmentation":[126],"resampling.":[128],"Using":[129],"proposed":[131],"mitigation":[132],"approaches,":[133],"see":[135],"improvement":[136,159],"IoU(\\%)":[138],"NDS(\\%)":[140],"metrics":[141,164],"from":[142,165],"71.3":[143],"75.6":[145],"80.6":[147],"83.7":[149],"model.":[153],"Similarly,":[154],"ViT,":[156],"observe":[158],"IoU":[161],"NDS":[163],"74.9":[166],"79.2":[168],"83.8":[170],"87.1.":[172],"This":[173],"contributes":[175],"developing":[177],"reliable":[178],"while":[180],"enhancing":[181],"inclusiveness":[182],"minority":[184],"classes":[185],"datasets.":[187]},"counts_by_year":[],"updated_date":"2026-03-11T14:59:36.786465","created_date":"2025-10-10T00:00:00"}