{"id":"https://openalex.org/W4415683070","doi":"https://doi.org/10.3390/a18110690","title":"DAFF-Net: A Dual-Branch Attention-Guided Feature Fusion Network for Vehicle Re-Identification","display_name":"DAFF-Net: A Dual-Branch Attention-Guided Feature Fusion Network for Vehicle Re-Identification","publication_year":2025,"publication_date":"2025-10-29","ids":{"openalex":"https://openalex.org/W4415683070","doi":"https://doi.org/10.3390/a18110690"},"language":"en","primary_location":{"id":"doi:10.3390/a18110690","is_oa":true,"landing_page_url":"https://doi.org/10.3390/a18110690","pdf_url":null,"source":{"id":"https://openalex.org/S190629608","display_name":"Algorithms","issn_l":"1999-4893","issn":["1999-4893"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Algorithms","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.3390/a18110690","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5114025813","display_name":"Yi Guo","orcid":null},"institutions":[{"id":"https://openalex.org/I189210763","display_name":"Yunnan University","ror":"https://ror.org/0040axw97","country_code":"CN","type":"education","lineage":["https://openalex.org/I189210763"]},{"id":"https://openalex.org/I4210152155","display_name":"Yunnan Investment Group (China)","ror":"https://ror.org/04zh2hd39","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210152155"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yi Guo","raw_affiliation_strings":["School of Information Science and Engineering, Yunnan University, Kunming 650504, China","Yunnan Key Laboratory of Digital Communications, Yunnan Communications Investment & Construction Group Co., Ltd., Kunming 650103, China"],"affiliations":[{"raw_affiliation_string":"School of Information Science and Engineering, Yunnan University, Kunming 650504, China","institution_ids":["https://openalex.org/I189210763"]},{"raw_affiliation_string":"Yunnan Key Laboratory of Digital Communications, Yunnan Communications Investment & Construction Group Co., Ltd., Kunming 650103, China","institution_ids":["https://openalex.org/I4210152155"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5077604527","display_name":"Guowu Yuan","orcid":"https://orcid.org/0000-0002-8449-6861"},"institutions":[{"id":"https://openalex.org/I189210763","display_name":"Yunnan University","ror":"https://ror.org/0040axw97","country_code":"CN","type":"education","lineage":["https://openalex.org/I189210763"]},{"id":"https://openalex.org/I4210152155","display_name":"Yunnan Investment Group (China)","ror":"https://ror.org/04zh2hd39","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210152155"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guowu Yuan","raw_affiliation_strings":["School of Information Science and Engineering, Yunnan University, Kunming 650504, China","Yunnan Key Laboratory of Digital Communications, Yunnan Communications Investment & Construction Group Co., Ltd., Kunming 650103, China"],"affiliations":[{"raw_affiliation_string":"School of Information Science and Engineering, Yunnan University, Kunming 650504, China","institution_ids":["https://openalex.org/I189210763"]},{"raw_affiliation_string":"Yunnan Key Laboratory of Digital Communications, Yunnan Communications Investment & Construction Group Co., Ltd., Kunming 650103, China","institution_ids":["https://openalex.org/I4210152155"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5027753920","display_name":"Wen Long Li","orcid":"https://orcid.org/0009-0004-2085-8511"},"institutions":[{"id":"https://openalex.org/I4210152155","display_name":"Yunnan Investment Group (China)","ror":"https://ror.org/04zh2hd39","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210152155"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wen Li","raw_affiliation_strings":["Yunnan Key Laboratory of Digital Communications, Yunnan Communications Investment & Construction Group Co., Ltd., Kunming 650103, China"],"affiliations":[{"raw_affiliation_string":"Yunnan Key Laboratory of Digital Communications, Yunnan Communications Investment & Construction Group Co., Ltd., Kunming 650103, China","institution_ids":["https://openalex.org/I4210152155"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100348588","display_name":"Hao Li","orcid":"https://orcid.org/0000-0002-4019-3420"},"institutions":[{"id":"https://openalex.org/I4210152155","display_name":"Yunnan Investment Group (China)","ror":"https://ror.org/04zh2hd39","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210152155"]},{"id":"https://openalex.org/I189210763","display_name":"Yunnan University","ror":"https://ror.org/0040axw97","country_code":"CN","type":"education","lineage":["https://openalex.org/I189210763"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hao Li","raw_affiliation_strings":["School of Information Science and Engineering, Yunnan University, Kunming 650504, China","Yunnan Key Laboratory of Digital Communications, Yunnan Communications Investment & Construction Group Co., Ltd., Kunming 650103, China"],"affiliations":[{"raw_affiliation_string":"School of Information Science and Engineering, Yunnan University, Kunming 650504, China","institution_ids":["https://openalex.org/I189210763"]},{"raw_affiliation_string":"Yunnan Key Laboratory of Digital Communications, Yunnan Communications Investment & Construction Group Co., Ltd., Kunming 650103, China","institution_ids":["https://openalex.org/I4210152155"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5114025813"],"corresponding_institution_ids":["https://openalex.org/I189210763","https://openalex.org/I4210152155"],"apc_list":{"value":1400,"currency":"CHF","value_usd":1515},"apc_paid":{"value":1400,"currency":"CHF","value_usd":1515},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.33452362,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"18","issue":"11","first_page":"690","last_page":"690"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.4950999915599823,"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":0.4950999915599823,"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":0.3037000000476837,"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/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.04569999873638153,"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/discriminative-model","display_name":"Discriminative model","score":0.8374999761581421},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.6995999813079834},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.6000999808311462},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.5659000277519226},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.5638999938964844},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5582000017166138},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.5382999777793884},{"id":"https://openalex.org/keywords/backbone-network","display_name":"Backbone network","score":0.5300999879837036}],"concepts":[{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.8374999761581421},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7759000062942505},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6995999813079834},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.631600022315979},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.6000999808311462},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.5659000277519226},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.5638999938964844},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5582000017166138},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.5382999777793884},{"id":"https://openalex.org/C88796919","wikidata":"https://www.wikidata.org/wiki/Q1142907","display_name":"Backbone network","level":2,"score":0.5300999879837036},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.484499990940094},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.3416999876499176},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.33340001106262207},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.3176000118255615},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.3174999952316284},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3172000050544739},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.3077999949455261},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2793999910354614},{"id":"https://openalex.org/C2781234732","wikidata":"https://www.wikidata.org/wiki/Q943505","display_name":"Fusion center","level":4,"score":0.27399998903274536},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.26910001039505005},{"id":"https://openalex.org/C2993807640","wikidata":"https://www.wikidata.org/wiki/Q103709453","display_name":"Attention network","level":2,"score":0.2660999894142151},{"id":"https://openalex.org/C101814296","wikidata":"https://www.wikidata.org/wiki/Q5439685","display_name":"Feature model","level":3,"score":0.2623000144958496},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.26030001044273376},{"id":"https://openalex.org/C2988224531","wikidata":"https://www.wikidata.org/wiki/Q20830730","display_name":"Network structure","level":2,"score":0.2590999901294708}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.3390/a18110690","is_oa":true,"landing_page_url":"https://doi.org/10.3390/a18110690","pdf_url":null,"source":{"id":"https://openalex.org/S190629608","display_name":"Algorithms","issn_l":"1999-4893","issn":["1999-4893"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Algorithms","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:3fa3a2a4cd184d338a614329356c86d1","is_oa":true,"landing_page_url":"https://doaj.org/article/3fa3a2a4cd184d338a614329356c86d1","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Algorithms, Vol 18, Iss 11, p 690 (2025)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.3390/a18110690","is_oa":true,"landing_page_url":"https://doi.org/10.3390/a18110690","pdf_url":null,"source":{"id":"https://openalex.org/S190629608","display_name":"Algorithms","issn_l":"1999-4893","issn":["1999-4893"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Algorithms","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W2204750386","https://openalex.org/W2253429366","https://openalex.org/W2756012011","https://openalex.org/W2789546350","https://openalex.org/W2954626788","https://openalex.org/W2955854238","https://openalex.org/W2980046511","https://openalex.org/W3016056282","https://openalex.org/W3031868989","https://openalex.org/W3033250778","https://openalex.org/W3035645942","https://openalex.org/W3092949934","https://openalex.org/W3096818755","https://openalex.org/W3197932138","https://openalex.org/W4205141934","https://openalex.org/W4205222395","https://openalex.org/W4214736485","https://openalex.org/W4226104430","https://openalex.org/W4295035973","https://openalex.org/W4313648799","https://openalex.org/W4372347372","https://openalex.org/W4383220270","https://openalex.org/W4386886326","https://openalex.org/W4389104901","https://openalex.org/W4391344852","https://openalex.org/W4406868330"],"related_works":[],"abstract_inverted_index":{"Vehicle":[0],"re-identification":[1,224],"(Re-ID)":[2],"is":[3,141],"a":[4,94,119],"critical":[5],"task":[6,17],"in":[7,25,30,41,52,180,222],"the":[8,36,47,102,134,138,145,152,165,173,185,198,203,206,216,219],"fields":[9],"of":[10,35,49,104,137,202,218],"intelligent":[11],"transportation":[12],"and":[13,39,56,76,90,129,149,159,167,189,194,212],"urban":[14],"surveillance.":[15],"This":[16],"faces":[18],"numerous":[19],"challenges,":[20],"such":[21],"as":[22,73],"significant":[23],"changes":[24],"shooting":[26],"angles,":[27],"strong":[28],"similarities":[29],"appearance":[31],"between":[32,88],"different":[33],"vehicles":[34],"same":[37],"model,":[38],"difficulties":[40],"modeling":[42],"fine-grained":[43],"differences.":[44],"To":[45],"overcome":[46],"shortcomings":[48],"existing":[50,177],"methods":[51,179],"local":[53,127],"feature":[54,65,80,106],"extraction":[55,81],"multi-scale":[57,113],"fusion,":[58],"this":[59],"paper":[60],"proposes":[61],"an":[62],"attention-guided":[63],"dual-branch":[64],"fusion":[66,107],"network":[67,70,153,221],"(DAFF-Net).":[68],"The":[69,109],"uses":[71],"ResNet50-ibn":[72],"its":[74],"backbone":[75],"designs":[77],"two":[78,146],"complementary":[79],"branches.":[82],"One":[83],"branch":[84,111,147],"fuses":[85],"cross-layer":[86,105],"attention":[87,114],"shallow":[89],"deep":[91],"features,":[92,117],"introducing":[93],"Temperature-Calibration":[95],"Attention":[96,122],"Fusion":[97],"Module":[98,123],"(TCAF)":[99],"to":[100,125,192],"improve":[101],"accuracy":[103],"effectively.":[108],"other":[110],"enhances":[112],"for":[115],"mid-layer":[116],"constructing":[118],"Multi-Scale":[120],"Gated":[121],"(MSGA)":[124],"extract":[126],"details":[128],"directional":[130],"structural":[131],"information.":[132],"Finally,":[133],"discriminative":[135],"ability":[136],"enhanced":[139],"features":[140,148],"improved":[142],"by":[143],"concatenating":[144],"jointly":[150],"optimizing":[151],"using":[154],"triplet":[155],"loss,":[156,158],"cross-entropy":[157],"center":[160],"loss.":[161],"Experimental":[162],"results":[163],"on":[164],"VeRi-776":[166,186],"VehicleID":[168,204],"public":[169],"datasets":[170],"indicate":[171],"that":[172],"proposed":[174,220],"DAFF-Net":[175],"outperforms":[176],"mainstream":[178],"multiple":[181],"key":[182],"metrics.":[183],"On":[184],"dataset,":[187,205],"mAP":[188],"CMC@1":[190,207],"increased":[191],"82.2%":[193],"97.5%,":[195],"respectively.":[196],"In":[197],"three":[199],"test":[200],"subsets":[201],"metric":[208],"achieved":[209],"90.7%,":[210],"84.6%,":[211],"82.1%,":[213],"respectively,":[214],"demonstrating":[215],"effectiveness":[217],"vehicle":[223],"tasks.":[225]},"counts_by_year":[],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2025-10-30T00:00:00"}