{"id":"https://openalex.org/W4413978379","doi":"https://doi.org/10.1109/tits.2025.3604365","title":"Enhancing Semi-Supervised Instance Segmentation Through SAM-Driven Pseudo-Label Generation in Autonomous Driving Environment","display_name":"Enhancing Semi-Supervised Instance Segmentation Through SAM-Driven Pseudo-Label Generation in Autonomous Driving Environment","publication_year":2025,"publication_date":"2025-09-04","ids":{"openalex":"https://openalex.org/W4413978379","doi":"https://doi.org/10.1109/tits.2025.3604365"},"language":"en","primary_location":{"id":"doi:10.1109/tits.2025.3604365","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tits.2025.3604365","pdf_url":null,"source":{"id":"https://openalex.org/S144771191","display_name":"IEEE Transactions on Intelligent Transportation Systems","issn_l":"1524-9050","issn":["1524-9050","1558-0016"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Intelligent Transportation Systems","raw_type":"journal-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/A5008904903","display_name":"Licong Guan","orcid":"https://orcid.org/0000-0002-8421-0018"},"institutions":[{"id":"https://openalex.org/I55538621","display_name":"China Jiliang University","ror":"https://ror.org/05v1y0t93","country_code":"CN","type":"education","lineage":["https://openalex.org/I55538621"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Licong Guan","raw_affiliation_strings":["College of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou, China"],"raw_orcid":"https://orcid.org/0000-0002-8421-0018","affiliations":[{"raw_affiliation_string":"College of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou, China","institution_ids":["https://openalex.org/I55538621"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5007002715","display_name":"Meng Ge","orcid":"https://orcid.org/0000-0002-3468-6149"},"institutions":[{"id":"https://openalex.org/I55538621","display_name":"China Jiliang University","ror":"https://ror.org/05v1y0t93","country_code":"CN","type":"education","lineage":["https://openalex.org/I55538621"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Meng Ge","raw_affiliation_strings":["College of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou, China"],"raw_orcid":"https://orcid.org/0000-0002-3468-6149","affiliations":[{"raw_affiliation_string":"College of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou, China","institution_ids":["https://openalex.org/I55538621"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5008010812","display_name":"Xue Yuan","orcid":null},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xue Yuan","raw_affiliation_strings":["School of Automation and Intelligence, Beijing Jiaotong University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-0963-1998","affiliations":[{"raw_affiliation_string":"School of Automation and Intelligence, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.3589,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.90925497,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":"26","issue":"12","first_page":"22680","last_page":"22689"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12016","display_name":"Web Data Mining and Analysis","score":0.9535999894142151,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T12016","display_name":"Web Data Mining and Analysis","score":0.9535999894142151,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T12707","display_name":"Vehicle License Plate Recognition","score":0.9467999935150146,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"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/T10601","display_name":"Handwritten Text Recognition Techniques","score":0.9455999732017517,"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":0.5953351855278015},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5900933742523193},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.586700439453125},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3815850019454956},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.36757129430770874}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5953351855278015},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5900933742523193},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.586700439453125},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3815850019454956},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.36757129430770874}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tits.2025.3604365","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tits.2025.3604365","pdf_url":null,"source":{"id":"https://openalex.org/S144771191","display_name":"IEEE Transactions on Intelligent Transportation Systems","issn_l":"1524-9050","issn":["1524-9050","1558-0016"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Intelligent Transportation Systems","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":41,"referenced_works":["https://openalex.org/W1861492603","https://openalex.org/W2340897893","https://openalex.org/W2921940552","https://openalex.org/W2963073217","https://openalex.org/W2963785012","https://openalex.org/W3023463084","https://openalex.org/W3035680157","https://openalex.org/W3094901756","https://openalex.org/W3106651317","https://openalex.org/W3112288498","https://openalex.org/W3158661000","https://openalex.org/W3160485208","https://openalex.org/W3172507542","https://openalex.org/W3173721678","https://openalex.org/W3193426425","https://openalex.org/W4200050447","https://openalex.org/W4214654781","https://openalex.org/W4226345352","https://openalex.org/W4293057377","https://openalex.org/W4293198053","https://openalex.org/W4312548937","https://openalex.org/W4312815172","https://openalex.org/W4319996400","https://openalex.org/W4366678299","https://openalex.org/W4383112609","https://openalex.org/W4387092360","https://openalex.org/W4387449263","https://openalex.org/W4389000668","https://openalex.org/W4389430914","https://openalex.org/W4390017901","https://openalex.org/W4390873379","https://openalex.org/W4390874575","https://openalex.org/W4391021462","https://openalex.org/W4392449894","https://openalex.org/W4392552857","https://openalex.org/W4394597871","https://openalex.org/W4402904231","https://openalex.org/W4402980275","https://openalex.org/W4404612908","https://openalex.org/W4407168545","https://openalex.org/W4408353093"],"related_works":["https://openalex.org/W2961085424","https://openalex.org/W4306674287","https://openalex.org/W2772917594","https://openalex.org/W2036807459","https://openalex.org/W2058170566","https://openalex.org/W2755342338","https://openalex.org/W2166024367","https://openalex.org/W3116076068","https://openalex.org/W2229312674","https://openalex.org/W2951359407"],"abstract_inverted_index":{"In":[0],"recent":[1],"years,":[2],"relying":[3],"on":[4,36,138],"large":[5,42],"amounts":[6],"of":[7,44,101,131,152,205],"labeled":[8,154],"data":[9,46,155,180],"in":[10,19,47,74,224],"specific":[11],"scenarios,":[12],"instance":[13,71],"segmentation":[14,72,99,133,222],"has":[15],"made":[16],"great":[17],"strides":[18],"autonomous":[20,75,226],"driving.":[21],"However,":[22],"labeling":[23],"sufficient":[24],"images":[25],"for":[26,38,68,219],"each":[27],"target":[28,49],"domain":[29,50],"scene":[30],"is":[31,80,117],"time-consuming":[32],"and":[33,97,126,143,208],"impractical.":[34],"Based":[35],"this,":[37],"effectively":[39,220],"utilizing":[40],"a":[41,59,111,162],"amount":[43],"unlabeled":[45],"the":[48,53,81,85,98,129,132,139,153,158,166,185,206,217,225],"to":[51,83,90,103,107,122,184,203],"improve":[52,128],"model":[54,88,195],"performance,":[55],"this":[56],"paper":[57],"proposes":[58],"Segment":[60],"Anything":[61],"Model":[62],"(SAM)-driven":[63],"Pseudo-Labels":[64],"gEneration":[65],"(SAMPLE)":[66],"framework":[67,79],"enhancing":[69],"semi-supervised":[70],"(SSIS)":[73],"driving":[76,227],"environments.":[77],"This":[78],"first":[82],"apply":[84],"visual":[86],"foundation":[87,218],"SAM":[89,102,121],"an":[91],"SSIS":[92],"framework,":[93],"using":[94,149],"precise":[95],"prompts":[96],"capabilities":[100],"generate":[104,123],"pseudo-labels.":[105],"Additionally,":[106],"mitigate":[108],"pseudo-label":[109],"noise,":[110],"Multi-Point":[112],"Enhancement":[113],"(MPE)":[114],"prompting":[115],"method":[116,160,190],"proposed,":[118],"which":[119],"enables":[120],"high-quality":[124],"pseudo-labels":[125],"thus":[127],"performance":[130],"model.":[134],"Experiments":[135],"were":[136],"conducted":[137],"public":[140],"datasets":[141],"Cityscapes":[142],"COCO.":[144],"The":[145],"results":[146],"show":[147],"that":[148],"only":[150],"5%":[151],"from":[156],"Cityscapes,":[157],"proposed":[159],"achieves":[161],"1.8%":[163],"improvement":[164],"over":[165],"current":[167],"state-of-the-art":[168],"GD":[169],"method.":[170],"On":[171],"COCO,":[172],"SAMPLE":[173],"consistently":[174],"surpasses":[175],"other":[176],"methods":[177],"across":[178],"various":[179],"percentages.":[181],"Furthermore,":[182],"compared":[183],"Mixture-of-Experts":[186],"(MoE)":[187],"method,":[188],"our":[189],"boasts":[191],"higher":[192],"accuracy,":[193],"reduces":[194],"size":[196],"by":[197,212],"8@,":[198],"decreases":[199],"GPU":[200],"memory":[201],"usage":[202],"1/3":[204],"original,":[207],"increases":[209],"inference":[210],"speed":[211],"110@.":[213],"These":[214],"improvements":[215],"lay":[216],"deploying":[221],"models":[223],"environment.":[228]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
