{"id":"https://openalex.org/W7148553821","doi":"https://doi.org/10.48550/arxiv.2604.00820","title":"Continual Vision-Language Learning for Remote Sensing: Benchmarking and Analysis","display_name":"Continual Vision-Language Learning for Remote Sensing: Benchmarking and Analysis","publication_year":2026,"publication_date":"2026-04-01","ids":{"openalex":"https://openalex.org/W7148553821","doi":"https://doi.org/10.48550/arxiv.2604.00820"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.00820","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.00820","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"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2604.00820","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5071887817","display_name":"Xingxing Weng","orcid":"https://orcid.org/0000-0002-3326-8439"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Weng, Xingxing","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5027870796","display_name":"Ruifeng Ni","orcid":"https://orcid.org/0000-0001-8315-7545"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ni, Ruifeng","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132826806","display_name":"Chao Pang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pang, Chao","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132766357","display_name":"XiangYu Hao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hao, XiangYu","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128018490","display_name":"Yishan Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Yishan","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132803095","display_name":"Xiaokang Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Xiaokang","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132790667","display_name":"Wei Xu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xu, Wei","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5132823001","display_name":"Gui-Song Xia","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xia, Gui-Song","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":8,"corresponding_author_ids":["https://openalex.org/A5071887817"],"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.4438000023365021,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.4438000023365021,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.41519999504089355,"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":0.0421999990940094,"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/benchmarking","display_name":"Benchmarking","score":0.8822000026702881},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6920999884605408},{"id":"https://openalex.org/keywords/limiting","display_name":"Limiting","score":0.5347999930381775},{"id":"https://openalex.org/keywords/modalities","display_name":"Modalities","score":0.5263000130653381},{"id":"https://openalex.org/keywords/forgetting","display_name":"Forgetting","score":0.4733000099658966},{"id":"https://openalex.org/keywords/modality","display_name":"Modality (human\u2013computer interaction)","score":0.3727000057697296},{"id":"https://openalex.org/keywords/interpretation","display_name":"Interpretation (philosophy)","score":0.3675999939441681},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.31209999322891235}],"concepts":[{"id":"https://openalex.org/C86251818","wikidata":"https://www.wikidata.org/wiki/Q816754","display_name":"Benchmarking","level":2,"score":0.8822000026702881},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6920999884605408},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.675000011920929},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5734000205993652},{"id":"https://openalex.org/C188198153","wikidata":"https://www.wikidata.org/wiki/Q1613840","display_name":"Limiting","level":2,"score":0.5347999930381775},{"id":"https://openalex.org/C2779903281","wikidata":"https://www.wikidata.org/wiki/Q6888026","display_name":"Modalities","level":2,"score":0.5263000130653381},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.49149999022483826},{"id":"https://openalex.org/C7149132","wikidata":"https://www.wikidata.org/wiki/Q1377840","display_name":"Forgetting","level":2,"score":0.4733000099658966},{"id":"https://openalex.org/C2780226545","wikidata":"https://www.wikidata.org/wiki/Q6888030","display_name":"Modality (human\u2013computer interaction)","level":2,"score":0.3727000057697296},{"id":"https://openalex.org/C527412718","wikidata":"https://www.wikidata.org/wiki/Q855395","display_name":"Interpretation (philosophy)","level":2,"score":0.3675999939441681},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.3172000050544739},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.31209999322891235},{"id":"https://openalex.org/C112930515","wikidata":"https://www.wikidata.org/wiki/Q4389547","display_name":"Risk analysis (engineering)","level":1,"score":0.30869999527931213},{"id":"https://openalex.org/C2780735816","wikidata":"https://www.wikidata.org/wiki/Q28324931","display_name":"Incremental learning","level":2,"score":0.3068999946117401},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.2915000021457672},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.28790000081062317},{"id":"https://openalex.org/C34585555","wikidata":"https://www.wikidata.org/wiki/Q1368723","display_name":"Learning curve","level":2,"score":0.28029999136924744},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.2799000144004822},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.2583000063896179},{"id":"https://openalex.org/C77967617","wikidata":"https://www.wikidata.org/wiki/Q4677561","display_name":"Active learning (machine learning)","level":2,"score":0.25760000944137573},{"id":"https://openalex.org/C136197465","wikidata":"https://www.wikidata.org/wiki/Q1729295","display_name":"Variety (cybernetics)","level":2,"score":0.25029999017715454}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.00820","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.00820","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"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.00820","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.00820","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"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Current":[0],"remote":[1,78],"sensing":[2,26,94],"vision-language":[3,75,119],"models":[4,120],"(RS":[5],"VLMs)":[6],"demonstrate":[7],"impressive":[8],"performance":[9],"in":[10,77,140],"image":[11],"interpretation":[12,92],"but":[13],"rely":[14],"on":[15],"static":[16],"training":[17],"data,":[18],"limiting":[19],"their":[20],"ability":[21],"to":[22,39,110,134,158],"accommodate":[23],"continuously":[24],"emerging":[25],"modalities":[27],"and":[28,58,96,107,144],"downstream":[29],"tasks.":[30],"This":[31],"exposes":[32],"a":[33,70],"fundamental":[34],"challenge:":[35],"enabling":[36],"RS":[37,54,135,159],"VLMs":[38,55],"continually":[40],"adapt":[41],"without":[42],"catastrophic":[43,122],"forgetting.":[44],"Despite":[45],"its":[46],"practical":[47],"importance,":[48],"the":[49,150],"continual":[50,74,113,129,154],"learning":[51,76,130,155],"capability":[52],"of":[53,117],"remains":[56],"underexplored,":[57],"no":[59],"dedicated":[60],"benchmark":[61,72],"currently":[62],"exists.":[63],"In":[64],"this":[65],"work,":[66],"we":[67],"present":[68],"CLeaRS,":[69],"comprehensive":[71],"for":[73,152],"sensing.":[79],"CLeaRS":[80],"comprises":[81],"10":[82],"curated":[83],"subsets":[84],"with":[85],"over":[86],"207k":[87],"image-text":[88],"pairs,":[89],"spanning":[90],"diverse":[91,118],"tasks,":[93],"modalities,":[95],"application":[97],"scenarios.":[98],"We":[99],"further":[100],"define":[101],"three":[102],"evaluation":[103],"protocols:":[104],"long-horizon,":[105],"modality-incremental,":[106],"task-incremental":[108],"settings,":[109],"systematically":[111],"assess":[112],"adaptation.":[114],"Extensive":[115],"benchmarking":[116],"reveals":[121],"forgetting":[123],"across":[124],"all":[125],"settings.":[126],"Moreover,":[127],"representative":[128],"methods,":[131],"when":[132],"adapted":[133],"VLMs,":[136],"exhibit":[137],"limited":[138],"effectiveness":[139],"handling":[141],"task,":[142],"instruction,":[143],"modality":[145],"transitions.":[146],"Our":[147],"findings":[148],"underscore":[149],"need":[151],"developing":[153],"methods":[156],"tailored":[157],"VLMs.":[160]},"counts_by_year":[],"updated_date":"2026-04-03T16:44:17.987007","created_date":"2026-04-03T00:00:00"}
