{"id":"https://openalex.org/W6910703601","doi":"https://doi.org/10.48550/arxiv.2410.08456","title":"A Unified Deep Semantic Expansion Framework for Domain-Generalized Person Re-identification","display_name":"A Unified Deep Semantic Expansion Framework for Domain-Generalized Person Re-identification","publication_year":2024,"publication_date":"2024-10-11","ids":{"openalex":"https://openalex.org/W6910703601","doi":"https://doi.org/10.48550/arxiv.2410.08456"},"language":"en","primary_location":{"id":"doi:10.48550/arxiv.2410.08456","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2410.08456","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article-journal"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2410.08456","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Ang, Eugene P. W.","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ang, Eugene P. W.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Lin, Shan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lin, Shan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":null,"display_name":"Kot, Alex C.","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kot, Alex C.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":true,"primary_topic":{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.901199996471405,"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":0.901199996471405,"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":0.039000000804662704,"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/T11448","display_name":"Face recognition and analysis","score":0.010300000198185444,"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/domain","display_name":"Domain (mathematical analysis)","score":0.6365000009536743},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.6345000267028809},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.6251999735832214},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5893999934196472},{"id":"https://openalex.org/keywords/semantic-feature","display_name":"Semantic feature","score":0.5065000057220459},{"id":"https://openalex.org/keywords/domain-adaptation","display_name":"Domain adaptation","score":0.4284000098705292},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.3889999985694885},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.3528999984264374},{"id":"https://openalex.org/keywords/feature-engineering","display_name":"Feature engineering","score":0.3499999940395355}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7962999939918518},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.6365000009536743},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.6345000267028809},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6251999735832214},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6244000196456909},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5893999934196472},{"id":"https://openalex.org/C2781122975","wikidata":"https://www.wikidata.org/wiki/Q16928266","display_name":"Semantic feature","level":2,"score":0.5065000057220459},{"id":"https://openalex.org/C2776434776","wikidata":"https://www.wikidata.org/wiki/Q19246213","display_name":"Domain adaptation","level":3,"score":0.4284000098705292},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3977000117301941},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3889999985694885},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.3528999984264374},{"id":"https://openalex.org/C2778827112","wikidata":"https://www.wikidata.org/wiki/Q22245680","display_name":"Feature engineering","level":3,"score":0.3499999940395355},{"id":"https://openalex.org/C90312973","wikidata":"https://www.wikidata.org/wiki/Q7449052","display_name":"Semantic data model","level":2,"score":0.3301999866962433},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.32589998841285706},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.3206000030040741},{"id":"https://openalex.org/C207685749","wikidata":"https://www.wikidata.org/wiki/Q2088941","display_name":"Domain knowledge","level":2,"score":0.31949999928474426},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.3149999976158142},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.298799991607666},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.2840000092983246},{"id":"https://openalex.org/C2775955345","wikidata":"https://www.wikidata.org/wiki/Q7449071","display_name":"Semantic mapping","level":2,"score":0.28349998593330383},{"id":"https://openalex.org/C130318100","wikidata":"https://www.wikidata.org/wiki/Q2268914","display_name":"Semantic similarity","level":2,"score":0.2678000032901764},{"id":"https://openalex.org/C37926939","wikidata":"https://www.wikidata.org/wiki/Q7449061","display_name":"Semantic equivalence","level":4,"score":0.26409998536109924},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.25440001487731934},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.2531000077724457},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2517000138759613},{"id":"https://openalex.org/C45493050","wikidata":"https://www.wikidata.org/wiki/Q7884934","display_name":"Unified Model","level":2,"score":0.25119999051094055},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.250900000333786}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2410.08456","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2410.08456","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article-journal"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2410.08456","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2410.08456","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article-journal"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions","score":0.46208012104034424}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Supervised":[0],"Person":[1,38,87],"Re-identification":[2,88],"(Person":[3],"ReID)":[4],"methods":[5,40],"have":[6,41,182],"achieved":[7],"excellent":[8],"performance":[9,24,46],"when":[10,26],"training":[11,69],"and":[12,127,153,205,220],"testing":[13],"within":[14],"one":[15,92,136],"camera":[16,30],"network.":[17],"However,":[18,55,145],"they":[19,181],"usually":[20],"suffer":[21],"from":[22,51],"considerable":[23],"degradation":[25],"applied":[27,107],"to":[28,99,108,163,171,185,215],"different":[29],"systems.":[31],"In":[32],"recent":[33],"years,":[34],"many":[35,75],"Domain":[36,85,131],"Adaptation":[37],"ReID":[39],"been":[42],"proposed,":[43],"achieving":[44],"impressive":[45],"without":[47],"requiring":[48],"labeled":[49],"data":[50,62],"the":[52,60,64,68,82,119,243],"target":[53,65,110],"domain.":[54],"these":[56,249],"approaches":[57],"still":[58],"need":[59],"unlabeled":[61],"of":[63,121,248],"domain":[66],"during":[67],"process,":[70],"making":[71],"them":[72],"impractical":[73],"in":[74,116,143,146,165,211,226,246],"real-world":[76],"scenarios.":[77],"Our":[78],"work":[79,148],"focuses":[80],"on":[81,176,190,235],"more":[83,94,236],"practical":[84],"Generalized":[86],"(DG-ReID)":[89],"problem.":[90],"Given":[91],"or":[93],"source":[95],"domains,":[96],"it":[97],"aims":[98],"learn":[100],"a":[101,183,212,222],"generalized":[102],"model":[103],"that":[104,139,151,202],"can":[105],"be":[106],"unseen":[109],"domains.":[111],"One":[112],"promising":[113],"research":[114],"direction":[115],"DG-ReID":[117,228],"is":[118,135],"use":[120],"implicit":[122,156,204],"deep":[123,157],"semantic":[124,158,207],"feature":[125,159,208],"expansion,":[126],"our":[128,199,233],"previous":[129],"method,":[130],"Embedding":[132],"Expansion":[133],"(DEX),":[134],"such":[137],"example":[138],"achieves":[140],"powerful":[141],"results":[142],"DG-ReID.":[144],"this":[147,191,217],"we":[149,193,231],"show":[150],"DEX":[152],"other":[154],"similar":[155],"expansion":[160,209],"methods,":[161],"due":[162],"limitations":[164],"their":[166,173],"proposed":[167],"loss":[168],"function,":[169],"fail":[170],"reach":[172],"full":[174],"potential":[175],"large":[177],"evaluation":[178],"benchmarks":[179,250],"as":[180],"tendency":[184],"saturate":[186],"too":[187],"early.":[188],"Leveraging":[189],"analysis,":[192],"propose":[194],"Unified":[195],"Deep":[196],"Semantic":[197],"Expansion,":[198],"novel":[200],"framework":[201,214],"unifies":[203],"explicit":[206],"techniques":[210],"single":[213],"mitigate":[216],"early":[218],"over-fitting":[219],"achieve":[221],"new":[223],"state-of-the-art":[224],"(SOTA)":[225],"all":[227,247],"benchmarks.":[229],"Further,":[230],"apply":[232],"method":[234],"general":[237],"image":[238],"retrieval":[239],"tasks,":[240],"also":[241],"surpassing":[242],"current":[244],"SOTA":[245],"by":[251],"wide":[252],"margins.":[253]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}