{"id":"https://openalex.org/W4411358704","doi":"https://doi.org/10.1109/tits.2025.3576327","title":"DMLViT: Dynamic Multi-Scale Local Vision Transformer for Object Counting in Congested Traffic Scenes","display_name":"DMLViT: Dynamic Multi-Scale Local Vision Transformer for Object Counting in Congested Traffic Scenes","publication_year":2025,"publication_date":"2025-06-17","ids":{"openalex":"https://openalex.org/W4411358704","doi":"https://doi.org/10.1109/tits.2025.3576327"},"language":"en","primary_location":{"id":"doi:10.1109/tits.2025.3576327","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tits.2025.3576327","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/A5010788392","display_name":"Chenxi Lin","orcid":"https://orcid.org/0000-0001-7734-6761"},"institutions":[{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chenxi Lin","raw_affiliation_strings":["School of Transportation, Southeast University, Nanjing, China"],"raw_orcid":"https://orcid.org/0000-0001-7734-6761","affiliations":[{"raw_affiliation_string":"School of Transportation, Southeast University, Nanjing, China","institution_ids":["https://openalex.org/I76569877"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5103264843","display_name":"Xiaojian Hu","orcid":"https://orcid.org/0000-0001-5799-0568"},"institutions":[{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaojian Hu","raw_affiliation_strings":["School of Transportation, Southeast University, Nanjing, China"],"raw_orcid":"https://orcid.org/0000-0001-5799-0568","affiliations":[{"raw_affiliation_string":"School of Transportation, Southeast University, Nanjing, China","institution_ids":["https://openalex.org/I76569877"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I76569877"],"apc_list":null,"apc_paid":null,"fwci":2.2966,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.88569736,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":98},"biblio":{"volume":"26","issue":"10","first_page":"16222","last_page":"16235"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9865999817848206,"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9865999817848206,"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":0.9835000038146973,"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/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9811999797821045,"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-vision","display_name":"Computer vision","score":0.6797134876251221},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6060183048248291},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5645903944969177},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.4255271255970001},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.20132118463516235},{"id":"https://openalex.org/keywords/cartography","display_name":"Cartography","score":0.12635019421577454}],"concepts":[{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6797134876251221},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6060183048248291},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5645903944969177},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.4255271255970001},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.20132118463516235},{"id":"https://openalex.org/C58640448","wikidata":"https://www.wikidata.org/wiki/Q42515","display_name":"Cartography","level":1,"score":0.12635019421577454}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tits.2025.3576327","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tits.2025.3576327","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":[{"id":"https://openalex.org/G262025747","display_name":null,"funder_award_id":"52272344","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G4551343182","display_name":null,"funder_award_id":"BE2023802","funder_id":"https://openalex.org/F4320327777","funder_display_name":"Jiangsu Provincial Key Research and Development Program"},{"id":"https://openalex.org/G6757455766","display_name":null,"funder_award_id":"KYCX24_0455","funder_id":"https://openalex.org/F4320327780","funder_display_name":"Key Research and Development Program of Jiangxi Province"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320327777","display_name":"Jiangsu Provincial Key Research and Development Program","ror":null},{"id":"https://openalex.org/F4320327780","display_name":"Key Research and Development Program of Jiangxi Province","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":53,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W1203557841","https://openalex.org/W2072232009","https://openalex.org/W2140051110","https://openalex.org/W2194775991","https://openalex.org/W2463631526","https://openalex.org/W2600230189","https://openalex.org/W2741077351","https://openalex.org/W2750413591","https://openalex.org/W2886443245","https://openalex.org/W2895051362","https://openalex.org/W2896421256","https://openalex.org/W2906991739","https://openalex.org/W2962921175","https://openalex.org/W2963037989","https://openalex.org/W2963163009","https://openalex.org/W2963351448","https://openalex.org/W2963499661","https://openalex.org/W2963693541","https://openalex.org/W2964209782","https://openalex.org/W2967069910","https://openalex.org/W3004672782","https://openalex.org/W3012297320","https://openalex.org/W3015469128","https://openalex.org/W3015687342","https://openalex.org/W3047585969","https://openalex.org/W3138516171","https://openalex.org/W3139633126","https://openalex.org/W3176047859","https://openalex.org/W3201018943","https://openalex.org/W3203845557","https://openalex.org/W4205890571","https://openalex.org/W4214665794","https://openalex.org/W4225264236","https://openalex.org/W4226334005","https://openalex.org/W4283312418","https://openalex.org/W4285285918","https://openalex.org/W4309868896","https://openalex.org/W4312443924","https://openalex.org/W4312613051","https://openalex.org/W4313007769","https://openalex.org/W4313182800","https://openalex.org/W4319663728","https://openalex.org/W4385252090","https://openalex.org/W4385346076","https://openalex.org/W4386071979","https://openalex.org/W4386075553","https://openalex.org/W4386819106","https://openalex.org/W4388469878","https://openalex.org/W4389666313","https://openalex.org/W4390874814","https://openalex.org/W4392066340","https://openalex.org/W4400381628"],"related_works":["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","https://openalex.org/W2079911747","https://openalex.org/W1969923398"],"abstract_inverted_index":{"Object":[0],"counting":[1,32,171,176],"in":[2,30,70,150],"congested":[3],"traffic":[4,11,15,71],"scenes":[5],"is":[6,61],"an":[7,79,156],"important":[8],"component":[9],"of":[10],"perception,":[12],"facilitating":[13],"urban":[14],"management":[16],"and":[17,110,173],"public":[18],"transportation":[19],"capacity":[20],"optimization.":[21],"Vision":[22,84],"Transformer":[23,85],"(ViT)":[24],"models":[25,55],"have":[26],"achieved":[27],"remarkable":[28],"performance":[29,185],"object":[31],"tasks":[33],"due":[34],"to":[35,38,63,93,116,127,131,160,187],"their":[36],"ability":[37],"capture":[39,162],"long-range":[40],"dependencies":[41],"among":[42],"diverse":[43],"image":[44],"blocks.":[45],"In":[46],"this":[47,75],"paper,":[48],"we":[49,77,99,154],"observe":[50],"that":[51,179],"most":[52],"existing":[53],"ViT-based":[54],"typically":[56],"employ":[57],"fixed-size":[58],"attention,":[59],"which":[60,87],"insufficient":[62],"address":[64],"the":[65,136,143,180],"significant":[66],"scale":[67,96],"variation":[68],"present":[69],"scenes.":[72],"To":[73],"overcome":[74],"issue,":[76],"propose":[78],"effective":[80],"Dynamic":[81],"Multi-scale":[82],"Local":[83],"(DMLViT)":[86],"introduces":[88],"a":[89],"priori":[90],"multi-scale":[91],"representation":[92],"effectively":[94],"handle":[95],"variation.":[97],"Specifically,":[98],"carry":[100],"out":[101],"self-attention":[102],"on":[103,168],"multiple":[104],"windows":[105],"around":[106],"each":[107],"query":[108],"token":[109],"dynamically":[111],"fused":[112],"these":[113],"attention":[114],"features":[115],"model":[117,151],"multi-range":[118],"spatial":[119,139],"dependencies.":[120],"Two":[121],"dynamic":[122],"aggregation":[123],"strategies":[124],"are":[125],"designed":[126],"assign":[128],"corresponding":[129],"weights":[130],"different":[132],"window":[133],"branches":[134],"along":[135],"channel":[137],"or":[138],"dimensions.":[140],"Furthermore,":[141],"as":[142],"original":[144],"feed-forward":[145],"network":[146],"(FFN)":[147],"has":[148],"limitation":[149],"local":[152,158,163],"information,":[153],"develop":[155],"efficient":[157],"FFN":[159],"better":[161],"dense":[164],"information.":[165],"Extensive":[166],"experiments":[167],"four":[169],"crowd":[170],"datasets":[172,177],"three":[174],"vehicle":[175],"demonstrate":[178],"proposed":[181],"DMLViT":[182],"exhibits":[183],"promising":[184],"compared":[186],"state-of-the-art":[188],"methods.":[189]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
