{"id":"https://openalex.org/W4416429297","doi":"https://doi.org/10.1109/tgrs.2025.3635418","title":"AdaptGeo: Parameter-Efficient Cross-View Geo-Localization via Frozen Foundation Model and Transformer Adapter","display_name":"AdaptGeo: Parameter-Efficient Cross-View Geo-Localization via Frozen Foundation Model and Transformer Adapter","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4416429297","doi":"https://doi.org/10.1109/tgrs.2025.3635418"},"language":null,"primary_location":{"id":"doi:10.1109/tgrs.2025.3635418","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2025.3635418","pdf_url":null,"source":{"id":"https://openalex.org/S111326731","display_name":"IEEE Transactions on Geoscience and Remote Sensing","issn_l":"0196-2892","issn":["0196-2892","1558-0644"],"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 Geoscience and Remote Sensing","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/A5081445645","display_name":"Chenxi Yin","orcid":"https://orcid.org/0009-0001-2906-2913"},"institutions":[{"id":"https://openalex.org/I116953780","display_name":"Tongji University","ror":"https://ror.org/03rc6as71","country_code":"CN","type":"education","lineage":["https://openalex.org/I116953780"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Changjiang Yin","raw_affiliation_strings":["College of Surveying and Geo-Informatics, Tongji University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"College of Surveying and Geo-Informatics, Tongji University, Shanghai, China","institution_ids":["https://openalex.org/I116953780"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101742079","display_name":"Junqi Luo","orcid":"https://orcid.org/0000-0002-4459-431X"},"institutions":[{"id":"https://openalex.org/I116953780","display_name":"Tongji University","ror":"https://ror.org/03rc6as71","country_code":"CN","type":"education","lineage":["https://openalex.org/I116953780"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Junqi Luo","raw_affiliation_strings":["College of Surveying and Geo-Informatics, Tongji University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"College of Surveying and Geo-Informatics, Tongji University, Shanghai, China","institution_ids":["https://openalex.org/I116953780"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5067353962","display_name":"Qin Ye","orcid":"https://orcid.org/0000-0002-1056-9159"},"institutions":[{"id":"https://openalex.org/I116953780","display_name":"Tongji University","ror":"https://ror.org/03rc6as71","country_code":"CN","type":"education","lineage":["https://openalex.org/I116953780"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qin Ye","raw_affiliation_strings":["College of Surveying and Geo-Informatics, Tongji University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"College of Surveying and Geo-Informatics, Tongji University, Shanghai, China","institution_ids":["https://openalex.org/I116953780"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5040860732","display_name":"Xiaohan Zhang","orcid":"https://orcid.org/0009-0009-7280-4669"},"institutions":[{"id":"https://openalex.org/I116953780","display_name":"Tongji University","ror":"https://ror.org/03rc6as71","country_code":"CN","type":"education","lineage":["https://openalex.org/I116953780"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaohan Zhang","raw_affiliation_strings":["College of Electronics and Information Engineering, Tongji University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"College of Electronics and Information Engineering, Tongji University, Shanghai, China","institution_ids":["https://openalex.org/I116953780"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5081445645"],"corresponding_institution_ids":["https://openalex.org/I116953780"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.36483315,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"63","issue":null,"first_page":"1","last_page":"19"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.40549999475479126,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.40549999475479126,"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":0.28630000352859497,"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/T10689","display_name":"Remote-Sensing Image Classification","score":0.061400000005960464,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"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/adapter","display_name":"Adapter (computing)","score":0.7421000003814697},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.6345000267028809},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.515500009059906},{"id":"https://openalex.org/keywords/bridging","display_name":"Bridging (networking)","score":0.5044999718666077},{"id":"https://openalex.org/keywords/drone","display_name":"Drone","score":0.5002999901771545},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.4472000002861023},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.3422999978065491}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8126000165939331},{"id":"https://openalex.org/C177284502","wikidata":"https://www.wikidata.org/wiki/Q1005390","display_name":"Adapter (computing)","level":2,"score":0.7421000003814697},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.6345000267028809},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5404999852180481},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.515500009059906},{"id":"https://openalex.org/C174348530","wikidata":"https://www.wikidata.org/wiki/Q188635","display_name":"Bridging (networking)","level":2,"score":0.5044999718666077},{"id":"https://openalex.org/C59519942","wikidata":"https://www.wikidata.org/wiki/Q650665","display_name":"Drone","level":2,"score":0.5002999901771545},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.4472000002861023},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.3422999978065491},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.3296000063419342},{"id":"https://openalex.org/C12186640","wikidata":"https://www.wikidata.org/wiki/Q6815743","display_name":"Memory model","level":3,"score":0.3140000104904175},{"id":"https://openalex.org/C115901376","wikidata":"https://www.wikidata.org/wiki/Q184199","display_name":"Automation","level":2,"score":0.30959999561309814},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.30640000104904175},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2838999927043915},{"id":"https://openalex.org/C207850805","wikidata":"https://www.wikidata.org/wiki/Q269608","display_name":"Reverse engineering","level":2,"score":0.2793000042438507},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.27469998598098755},{"id":"https://openalex.org/C108882727","wikidata":"https://www.wikidata.org/wiki/Q2991685","display_name":"Solid modeling","level":2,"score":0.2671000063419342}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tgrs.2025.3635418","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2025.3635418","pdf_url":null,"source":{"id":"https://openalex.org/S111326731","display_name":"IEEE Transactions on Geoscience and Remote Sensing","issn_l":"0196-2892","issn":["0196-2892","1558-0644"],"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 Geoscience and Remote Sensing","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":48,"referenced_works":["https://openalex.org/W2199890863","https://openalex.org/W2802080802","https://openalex.org/W2963474852","https://openalex.org/W2997129483","https://openalex.org/W3035158519","https://openalex.org/W3081227581","https://openalex.org/W3092933908","https://openalex.org/W3099793224","https://openalex.org/W3115894062","https://openalex.org/W3153675281","https://openalex.org/W3159481202","https://openalex.org/W3179267034","https://openalex.org/W3205797353","https://openalex.org/W3214744507","https://openalex.org/W4205635926","https://openalex.org/W4206178588","https://openalex.org/W4206760693","https://openalex.org/W4213147678","https://openalex.org/W4214520160","https://openalex.org/W4229453513","https://openalex.org/W4312290555","https://openalex.org/W4312646610","https://openalex.org/W4312651322","https://openalex.org/W4312703192","https://openalex.org/W4312854990","https://openalex.org/W4312884055","https://openalex.org/W4313156423","https://openalex.org/W4315778358","https://openalex.org/W4322576645","https://openalex.org/W4382316430","https://openalex.org/W4384519221","https://openalex.org/W4386057714","https://openalex.org/W4387812957","https://openalex.org/W4390190100","https://openalex.org/W4390691253","https://openalex.org/W4390873434","https://openalex.org/W4390874575","https://openalex.org/W4392616988","https://openalex.org/W4393154934","https://openalex.org/W4396594805","https://openalex.org/W4396834435","https://openalex.org/W4396914836","https://openalex.org/W4399572612","https://openalex.org/W4400448300","https://openalex.org/W4402199568","https://openalex.org/W4403598545","https://openalex.org/W4404057073","https://openalex.org/W4409369448"],"related_works":[],"abstract_inverted_index":{"Cross-view":[0],"geo-localization":[1],"aims":[2],"to":[3,36,64,117,143],"retrieve":[4],"images":[5],"of":[6],"the":[7,70],"same":[8],"location":[9],"captured":[10],"from":[11],"different":[12],"viewpoints.":[13],"Recent":[14],"deep":[15],"learning":[16,86,91],"methods":[17],"have":[18],"improved":[19],"this":[20,41],"task.":[21],"However,":[22],"most":[23],"rely":[24],"on":[25,108],"full":[26,119],"model":[27,54,120],"fine-tuning,":[28],"which":[29],"demands":[30],"large":[31],"memory":[32],"and":[33,75,104,110],"storage":[34],"due":[35],"extensive":[37],"parameters.":[38],"To":[39],"address":[40],"limitation,":[42],"we":[43],"propose":[44],"AdaptGeo,":[45],"a":[46,84],"parameter-efficient":[47],"framework":[48,79],"that":[49,88],"adapts":[50],"frozen":[51,96],"vision":[52],"foundation":[53,141],"through":[55],"lightweight":[56],"transformer-based":[57],"adapter.":[58],"The":[59,78,131],"adapter":[60],"employs":[61],"multi-head":[62],"attention":[63],"learn":[65],"viewpoint-invariant":[66],"representations,":[67],"effectively":[68],"bridging":[69],"domain":[71],"gap":[72],"between":[73],"drone":[74],"satellite":[76],"imagery.":[77],"is":[80],"further":[81],"enhanced":[82],"by":[83,128],"multi-objective":[85],"strategy":[87],"integrates":[89],"contrastive":[90],"with":[92],"location-specific":[93],"supervision.":[94],"A":[95],"backbone":[97],"also":[98],"allows":[99],"feature":[100],"pre-extraction,":[101],"reducing":[102],"training":[103],"inference":[105],"cost.":[106],"Experiments":[107],"University-1652":[109],"SUES-200":[111],"datasets":[112],"demonstrate":[113],"competitive":[114],"performance.":[115],"Compared":[116],"traditional":[118],"fine-tuning":[121],"approaches,":[122],"our":[123],"method":[124],"reduces":[125],"trainable":[126],"parameters":[127],"over":[129],"87%.":[130],"proposed":[132],"approach":[133],"provides":[134],"an":[135],"efficient":[136],"solution":[137],"for":[138],"adapting":[139],"visual":[140],"models":[142],"cross-view":[144],"geo-localization.":[145]},"counts_by_year":[],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-11-20T00:00:00"}
