{"id":"https://openalex.org/W4415708061","doi":"https://doi.org/10.1109/icme59968.2025.11210054","title":"Dynamic Token Selective Transformer for Aerial-Ground Person Re-Identification","display_name":"Dynamic Token Selective Transformer for Aerial-Ground Person Re-Identification","publication_year":2025,"publication_date":"2025-06-30","ids":{"openalex":"https://openalex.org/W4415708061","doi":"https://doi.org/10.1109/icme59968.2025.11210054"},"language":null,"primary_location":{"id":"doi:10.1109/icme59968.2025.11210054","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icme59968.2025.11210054","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Multimedia and Expo (ICME)","raw_type":"proceedings-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/A5100622297","display_name":"Yuhai Wang","orcid":"https://orcid.org/0000-0002-8251-7948"},"institutions":[{"id":"https://openalex.org/I1174212","display_name":"University of Southern California","ror":"https://ror.org/03taz7m60","country_code":"US","type":"education","lineage":["https://openalex.org/I1174212"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yuhai Wang","raw_affiliation_strings":["University of Southern California,Department of Computer Science,Los Angeles,United States"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Southern California,Department of Computer Science,Los Angeles,United States","institution_ids":["https://openalex.org/I1174212"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5054326900","display_name":"Maryam Pishgar","orcid":"https://orcid.org/0009-0003-7159-3245"},"institutions":[{"id":"https://openalex.org/I1174212","display_name":"University of Southern California","ror":"https://ror.org/03taz7m60","country_code":"US","type":"education","lineage":["https://openalex.org/I1174212"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Maryam Pishgar","raw_affiliation_strings":["University of Southern California,Department of Industrial and Systems Engineering,Los Angeles,United States"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Southern California,Department of Industrial and Systems Engineering,Los Angeles,United States","institution_ids":["https://openalex.org/I1174212"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I1174212"],"apc_list":null,"apc_paid":null,"fwci":0.8823,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.79457834,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.6320000290870667,"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":0.6320000290870667,"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/T11133","display_name":"UAV Applications and Optimization","score":0.08590000122785568,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace Engineering"},"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/T10036","display_name":"Advanced Neural Network Applications","score":0.06960000097751617,"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/security-token","display_name":"Security token","score":0.9093000292778015},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.5521000027656555},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4284999966621399},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.4133000075817108},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.40560001134872437},{"id":"https://openalex.org/keywords/identity","display_name":"Identity (music)","score":0.3944999873638153}],"concepts":[{"id":"https://openalex.org/C48145219","wikidata":"https://www.wikidata.org/wiki/Q1335365","display_name":"Security token","level":2,"score":0.9093000292778015},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.767300009727478},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.5521000027656555},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4284999966621399},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.4133000075817108},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.40560001134872437},{"id":"https://openalex.org/C2778355321","wikidata":"https://www.wikidata.org/wiki/Q17079427","display_name":"Identity (music)","level":2,"score":0.3944999873638153},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.39419999718666077},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.36399999260902405},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.35260000824928284},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.34360000491142273},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2930000126361847},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.2913999855518341},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.27140000462532043},{"id":"https://openalex.org/C2988416141","wikidata":"https://www.wikidata.org/wiki/Q6031139","display_name":"Information loss","level":2,"score":0.2605000138282776},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.25940001010894775}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icme59968.2025.11210054","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icme59968.2025.11210054","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Multimedia and Expo (ICME)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W2204750386","https://openalex.org/W2795758732","https://openalex.org/W2953684117","https://openalex.org/W2963047834","https://openalex.org/W2967515867","https://openalex.org/W2998792609","https://openalex.org/W3136038792","https://openalex.org/W3207271111","https://openalex.org/W4226407477","https://openalex.org/W4283821305","https://openalex.org/W4312299780","https://openalex.org/W4312680544","https://openalex.org/W4380838989","https://openalex.org/W4385261162","https://openalex.org/W4385767786","https://openalex.org/W4386160069","https://openalex.org/W4387969510","https://openalex.org/W4390872206","https://openalex.org/W4390970425","https://openalex.org/W4391896102","https://openalex.org/W4392114244","https://openalex.org/W4402704575","https://openalex.org/W4402979709"],"related_works":[],"abstract_inverted_index":{"Aerial-Ground":[0],"Person":[1],"Re-Identification":[2],"(AGPReID)":[3],"holds":[4],"significant":[5,99],"practical":[6],"value":[7],"but":[8],"faces":[9],"unique":[10,83],"challenges":[11],"due":[12],"to":[13,37,41,64,115,156],"pronounced":[14],"variations":[15],"in":[16],"viewing":[17],"angles,":[18],"lighting":[19],"conditions,":[20],"and":[21,39,175,188],"background":[22],"interference.":[23],"Traditional":[24],"methods,":[25],"often":[26],"involving":[27],"a":[28,49,82,91],"global":[29],"analysis":[30],"of":[31,125,136,167,170,177],"the":[32,72,88,96,123,134,144,157,165],"entire":[33],"image,":[34],"frequently":[35],"lead":[36],"inefficiencies":[38],"susceptibility":[40],"irrelevant":[42],"data.":[43],"In":[44,160],"this":[45],"paper,":[46],"we":[47,70,94,162],"propose":[48],"novel":[50],"Dynamic":[51],"Token":[52],"Selective":[53],"Transformer":[54],"(DTST)":[55],"tailored":[56],"for":[57,106,186],"AGPReID,":[58],"which":[59],"dynamically":[60],"selects":[61],"pivotal":[62],"tokens":[63,100],"concentrate":[65],"on":[66,130,143,179],"pertinent":[67],"regions.":[68],"Specifically,":[69],"segment":[71],"input":[73],"image":[74],"into":[75],"multiple":[76],"tokens,":[77,120,171],"with":[78],"each":[79],"token":[80,172],"representing":[81],"region":[84],"or":[85],"feature":[86],"within":[87],"image.":[89],"Using":[90],"Top-k":[92],"strategy,":[93],"extract":[95],"k":[97],"most":[98],"that":[101],"contain":[102],"vital":[103],"information":[104],"essential":[105],"identity":[107,126],"recognition.":[108],"Subsequently,":[109],"an":[110],"attention":[111],"mechanism":[112],"is":[113],"employed":[114],"discern":[116],"interrelations":[117],"among":[118],"diverse":[119],"thereby":[121],"enhancing":[122],"representation":[124],"features.":[127],"Extensive":[128],"experiments":[129],"benchmark":[131],"datasets":[132],"showcases":[133],"superiority":[135],"our":[137,147,184],"method":[138,149],"over":[139],"existing":[140],"works.":[141],"Notably,":[142],"CARGO":[145],"dataset,":[146],"proposed":[148],"gains":[150],"1.18%":[151],"mAP":[152],"improvements":[153],"when":[154],"compared":[155],"second":[158],"place.":[159],"addition,":[161],"comprehensively":[163],"analyze":[164],"impact":[166],"different":[168],"numbers":[169,176],"insertion":[173],"positions,":[174],"heads":[178],"model":[180],"performance.":[181],"Please":[182],"checkout":[183],"website":[185],"code":[187],"dataset:":[189],"https://yuhaiw.github.io/DTS-AGPReID/":[190]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2025-10-30T00:00:00"}