{"id":"https://openalex.org/W7164337523","doi":"https://doi.org/10.48550/arxiv.2606.12258","title":"Bridging Day and Night: Unsupervised Cross-Domain Re-Identification with Synergistic Prompt and Prototype Learning","display_name":"Bridging Day and Night: Unsupervised Cross-Domain Re-Identification with Synergistic Prompt and Prototype Learning","publication_year":2026,"publication_date":"2026-06-10","ids":{"openalex":"https://openalex.org/W7164337523","doi":"https://doi.org/10.48550/arxiv.2606.12258"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.12258","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.12258","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":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2606.12258","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5103539645","display_name":"Jiyang Xu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xu, Jiyang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138427453","display_name":"Rui Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Rui","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5138398629","display_name":"Hang Dai","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dai, Hang","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":false,"primary_topic":{"id":"https://openalex.org/T11714","display_name":"Multimodal Machine Learning Applications","score":0.163100004196167,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.163100004196167,"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.13330000638961792,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.12110000103712082,"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/bridging","display_name":"Bridging (networking)","score":0.7046999931335449},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.671500027179718},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.593999981880188},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.5583000183105469},{"id":"https://openalex.org/keywords/unsupervised-learning","display_name":"Unsupervised learning","score":0.5300999879837036},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.4984000027179718},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.48339998722076416},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.47040000557899475},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4406999945640564}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7892000079154968},{"id":"https://openalex.org/C174348530","wikidata":"https://www.wikidata.org/wiki/Q188635","display_name":"Bridging (networking)","level":2,"score":0.7046999931335449},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.671500027179718},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6274999976158142},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.593999981880188},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.5583000183105469},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.5300999879837036},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.4984000027179718},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.48339998722076416},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.47040000557899475},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4648999869823456},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4406999945640564},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.40450000762939453},{"id":"https://openalex.org/C2778355321","wikidata":"https://www.wikidata.org/wiki/Q17079427","display_name":"Identity (music)","level":2,"score":0.35179999470710754},{"id":"https://openalex.org/C2779321571","wikidata":"https://www.wikidata.org/wiki/Q7936605","display_name":"Visual learning","level":2,"score":0.3456999957561493},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3197000026702881},{"id":"https://openalex.org/C142853389","wikidata":"https://www.wikidata.org/wiki/Q744778","display_name":"Association (psychology)","level":2,"score":0.3156000077724457},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.2818000018596649},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.2799000144004822},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.27320000529289246},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.27149999141693115},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.2651999890804291},{"id":"https://openalex.org/C86034646","wikidata":"https://www.wikidata.org/wiki/Q474311","display_name":"Semantic gap","level":4,"score":0.2635999917984009},{"id":"https://openalex.org/C2983787585","wikidata":"https://www.wikidata.org/wiki/Q93586","display_name":"Feature matching","level":3,"score":0.2623000144958496},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.2556000053882599},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.2529999911785126},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.25189998745918274}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.12258","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.12258","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":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2606.12258","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.12258","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":"Preprint"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","score":0.7015419602394104,"display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Cross-domain":[0],"day-night":[1,42],"re-identification":[2],"(ReID)":[3],"is":[4],"fundamentally":[5],"challenged":[6],"by":[7],"the":[8,76,81,123,180,186],"substantial":[9],"visual":[10,101],"appearance":[11],"discrepancies":[12],"between":[13],"daytime":[14],"and":[15,30,44,54,103,132,150,161,172],"nighttime":[16],"scenes.":[17],"Existing":[18],"fully":[19,197],"supervised":[20,198],"methods":[21],"rely":[22],"heavily":[23],"on":[24,176],"labor-intensive":[25],"annotations,":[26],"which":[27],"are":[28],"costly":[29],"exhibit":[31],"limited":[32],"generalization":[33],"across":[34,61,170],"domains.":[35],"In":[36,75,122],"this":[37],"work,":[38],"we":[39,79,126],"investigate":[40],"unsupervised":[41,187],"ReID":[43],"propose":[45],"a":[46,70,107,152],"novel":[47],"framework":[48,190],"that":[49],"synergistically":[50],"combines":[51],"prompt":[52],"learning":[53,57],"prototype-based":[55],"representation":[56],"to":[58,84,99,143,157,195],"associate":[59],"identities":[60],"domains":[62],"without":[63],"requiring":[64],"manual":[65],"labels.":[66],"Our":[67],"approach":[68],"follows":[69],"progressive":[71],"two-stage":[72],"training":[73],"strategy.":[74],"first":[77],"stage,":[78,125],"exploit":[80],"vision-language":[82],"model":[83],"generate":[85],"instance-specific":[86],"textual":[87,104],"prompts":[88,105,117],"in":[89],"an":[90,95,138],"annotation-free":[91],"manner.":[92],"We":[93],"employ":[94],"instance-level":[96],"alignment":[97],"mechanism":[98],"embed":[100],"features":[102],"into":[106],"unified":[108],"semantic":[109],"space,":[110],"aligning":[111],"unlabeled":[112],"day/night":[113],"images":[114],"with":[115],"learnable":[116],"via":[118],"instance-aware":[119],"dynamic-bias":[120],"adaptation.":[121],"second":[124],"construct":[127],"domain-specific":[128],"prototype":[129,154,163],"memory":[130],"banks":[131],"introduce":[133],"two":[134],"complementary":[135],"modules:":[136],"i)":[137],"intra-domain":[139],"identity":[140,168],"association":[141],"module":[142,156],"enhance":[144],"feature":[145],"discriminability":[146],"within":[147],"each":[148],"domain,":[149],"ii)":[151],"cross-domain":[153],"matching":[155],"reliably":[158],"identify":[159],"positive":[160],"negative":[162],"pairs,":[164],"thereby":[165],"establishing":[166],"robust":[167],"correspondences":[169],"day":[171],"night.":[173],"Extensive":[174],"experiments":[175],"public":[177],"benchmarks":[178],"validate":[179],"effectiveness":[181],"of":[182],"our":[183,189],"method.":[184],"Under":[185],"setting,":[188],"attains":[191],"Rank-1":[192],"accuracy":[193],"comparable":[194],"state-of-the-art":[196],"methods.":[199]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-12T00:00:00"}
