{"id":"https://openalex.org/W4401414082","doi":"https://doi.org/10.1109/icra57147.2024.10611122","title":"BaSAL: Size-Balanced Warm Start Active Learning for LiDAR Semantic Segmentation","display_name":"BaSAL: Size-Balanced Warm Start Active Learning for LiDAR Semantic Segmentation","publication_year":2024,"publication_date":"2024-05-13","ids":{"openalex":"https://openalex.org/W4401414082","doi":"https://doi.org/10.1109/icra57147.2024.10611122"},"language":"en","primary_location":{"id":"doi:10.1109/icra57147.2024.10611122","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icra57147.2024.10611122","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Robotics and Automation (ICRA)","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/A5101269919","display_name":"Jiarong Wei","orcid":null},"institutions":[{"id":"https://openalex.org/I98358874","display_name":"Delft University of Technology","ror":"https://ror.org/02e2c7k09","country_code":"NL","type":"education","lineage":["https://openalex.org/I98358874"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Jiarong Wei","raw_affiliation_strings":["Delft University of Technology,Intelligent Vehicles Group,the Netherlands"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Delft University of Technology,Intelligent Vehicles Group,the Netherlands","institution_ids":["https://openalex.org/I98358874"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014127404","display_name":"Yancong Lin","orcid":null},"institutions":[{"id":"https://openalex.org/I98358874","display_name":"Delft University of Technology","ror":"https://ror.org/02e2c7k09","country_code":"NL","type":"education","lineage":["https://openalex.org/I98358874"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Yancong Lin","raw_affiliation_strings":["Delft University of Technology,Intelligent Vehicles Group,the Netherlands"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Delft University of Technology,Intelligent Vehicles Group,the Netherlands","institution_ids":["https://openalex.org/I98358874"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5001917215","display_name":"Holger Caesar","orcid":"https://orcid.org/0000-0001-5099-6297"},"institutions":[{"id":"https://openalex.org/I98358874","display_name":"Delft University of Technology","ror":"https://ror.org/02e2c7k09","country_code":"NL","type":"education","lineage":["https://openalex.org/I98358874"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Holger Caesar","raw_affiliation_strings":["Delft University of Technology,Intelligent Vehicles Group,the Netherlands"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Delft University of Technology,Intelligent Vehicles Group,the Netherlands","institution_ids":["https://openalex.org/I98358874"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I98358874"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"18258","last_page":"18264"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12072","display_name":"Machine Learning and Algorithms","score":0.9998999834060669,"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"}},"topics":[{"id":"https://openalex.org/T12072","display_name":"Machine Learning and Algorithms","score":0.9998999834060669,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9944000244140625,"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/T10653","display_name":"Robot Manipulation and Learning","score":0.9898999929428101,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems 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/lidar","display_name":"Lidar","score":0.6846827268600464},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6585015654563904},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6271328926086426},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4733410179615021},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.4604656398296356},{"id":"https://openalex.org/keywords/active-learning","display_name":"Active learning (machine learning)","score":0.44084277749061584},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.23429372906684875},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.17400076985359192}],"concepts":[{"id":"https://openalex.org/C51399673","wikidata":"https://www.wikidata.org/wiki/Q504027","display_name":"Lidar","level":2,"score":0.6846827268600464},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6585015654563904},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6271328926086426},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4733410179615021},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4604656398296356},{"id":"https://openalex.org/C77967617","wikidata":"https://www.wikidata.org/wiki/Q4677561","display_name":"Active learning (machine learning)","level":2,"score":0.44084277749061584},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.23429372906684875},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.17400076985359192},{"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/icra57147.2024.10611122","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icra57147.2024.10611122","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Robotics and Automation (ICRA)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":58,"referenced_works":["https://openalex.org/W1490308602","https://openalex.org/W1542068205","https://openalex.org/W2011674654","https://openalex.org/W2098742124","https://openalex.org/W2099550922","https://openalex.org/W2132948751","https://openalex.org/W2150066425","https://openalex.org/W2165086927","https://openalex.org/W2171671120","https://openalex.org/W2548061775","https://openalex.org/W2601243251","https://openalex.org/W2774918944","https://openalex.org/W2798820905","https://openalex.org/W2903158431","https://openalex.org/W2948367246","https://openalex.org/W2951061410","https://openalex.org/W2956371155","https://openalex.org/W2963125977","https://openalex.org/W2963238274","https://openalex.org/W2964059111","https://openalex.org/W2986514296","https://openalex.org/W2991216808","https://openalex.org/W3009763662","https://openalex.org/W3034235207","https://openalex.org/W3035574168","https://openalex.org/W3047263508","https://openalex.org/W3101345273","https://openalex.org/W3109154950","https://openalex.org/W3169120894","https://openalex.org/W3198790326","https://openalex.org/W3203240897","https://openalex.org/W3203978358","https://openalex.org/W4221138759","https://openalex.org/W4237744189","https://openalex.org/W4281492306","https://openalex.org/W4281951312","https://openalex.org/W4286897059","https://openalex.org/W4287236013","https://openalex.org/W4296120661","https://openalex.org/W4312350487","https://openalex.org/W4312376118","https://openalex.org/W4312812384","https://openalex.org/W4391591475","https://openalex.org/W4400647068","https://openalex.org/W6632271312","https://openalex.org/W6644929900","https://openalex.org/W6679716015","https://openalex.org/W6730042731","https://openalex.org/W6735374517","https://openalex.org/W6747231328","https://openalex.org/W6756615331","https://openalex.org/W6763607942","https://openalex.org/W6764588566","https://openalex.org/W6774700101","https://openalex.org/W6792663124","https://openalex.org/W6802568005","https://openalex.org/W6810375867","https://openalex.org/W6838838199"],"related_works":["https://openalex.org/W4319317934","https://openalex.org/W2901265155","https://openalex.org/W2956374172","https://openalex.org/W4281783339","https://openalex.org/W4319837668","https://openalex.org/W4308071650","https://openalex.org/W3188333020","https://openalex.org/W1964041166","https://openalex.org/W4390887692","https://openalex.org/W4210818033"],"abstract_inverted_index":{"Active":[0],"learning":[1,43,67,112,222,232],"strives":[2],"to":[3,16,63,87,94,131,149,178,205],"reduce":[4],"the":[5,18,52,65,96,116,167,180,183,208,217,227],"need":[6],"for":[7,45],"costly":[8],"data":[9,74,84],"annotation,":[10],"by":[11,186],"repeatedly":[12],"querying":[13],"an":[14],"annotator":[15],"label":[17],"most":[19],"informative":[20],"samples":[21],"from":[22,33,81],"a":[23,31,107,123,138,162,187],"pool":[24],"of":[25,182,216],"unlabeled":[26],"data,":[27],"and":[28,193],"then":[29],"training":[30,206],"model":[32,80,185],"these":[34,102],"samples.":[35],"We":[36,224],"identify":[37],"two":[38],"problems":[39,103],"with":[40,196],"existing":[41,150,220,228],"active":[42,66,111,221,231],"methods":[44],"LiDAR":[46,58],"semantic":[47,59],"segmentation.":[48],"First,":[49],"they":[50,76],"overlook":[51],"severe":[53],"class":[54,121],"imbalance":[55],"inherent":[56],"in":[57,147,230],"segmentation":[60],"datasets.":[61],"Second,":[62],"bootstrap":[64],"loop":[68],"when":[69],"there":[70],"is":[71,92,142,237],"no":[72],"labeled":[73],"available,":[75],"train":[77],"their":[78,132],"initial":[79,184],"randomly":[82],"selected":[83],"samples,":[85],"leading":[86],"low":[88],"performance.":[89],"This":[90],"situation":[91],"referred":[93],"as":[95],"cold":[97,168],"start":[98,110,169,192],"problem.":[99],"To":[100],"address":[101],"we":[104,134,175],"propose":[105],"BaSAL,":[106],"size-balanced":[108,139,194],"warm":[109,191],"model,":[113,164],"based":[114],"on":[115,207,233],"observation":[117],"that":[118,141,174],"each":[119],"object":[120,128],"has":[122],"characteristic":[124],"size.":[125],"By":[126],"sampling":[127,158,195],"clusters":[129],"according":[130],"size,":[133],"can":[135],"thus":[136,165],"create":[137],"dataset":[140],"also":[143,225],"more":[144],"class-balanced.":[145],"Furthermore,":[146],"contrast":[148],"information":[151,198],"measures":[152],"like":[153],"entropy":[154],"or":[155],"CoreSet,":[156],"size-based":[157],"does":[159],"not":[160],"require":[161],"pretrained":[163],"addressing":[166],"problem":[170],"effectively.":[171],"Results":[172],"show":[173],"are":[176],"able":[177],"improve":[179],"performance":[181,204],"large":[188],"margin.":[189],"Combining":[190],"established":[197],"measures,":[199],"our":[200],"approach":[201],"achieves":[202],"comparable":[203],"entire":[209],"SemanticKITTI":[210],"dataset,":[211],"despite":[212],"using":[213],"only":[214],"5%":[215],"annotations,":[218],"outperforming":[219],"methods.":[223],"match":[226],"state-of-the-art":[229],"nuScenes.":[234],"Our":[235],"code":[236],"available":[238],"at:":[239],"https://github.com/Tony-WJR/BaSAL.":[240]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}