{"id":"https://openalex.org/W7163156294","doi":"https://doi.org/10.48550/arxiv.2606.00556","title":"Improving Visual Grounding in Remote Sensing via Cluster-Guided Refinement and Model Ensemble Voting","display_name":"Improving Visual Grounding in Remote Sensing via Cluster-Guided Refinement and Model Ensemble Voting","publication_year":2026,"publication_date":"2026-05-30","ids":{"openalex":"https://openalex.org/W7163156294","doi":"https://doi.org/10.48550/arxiv.2606.00556"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.00556","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.00556","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","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.2606.00556","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5123714820","display_name":"Panav Shah","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shah, Panav","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5038807432","display_name":"Geet Sethi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sethi, Geet","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5137618175","display_name":"Ashutosh Gandhe","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gandhe, Ashutosh","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":0.8428000211715698,"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":0.8428000211715698,"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":0.05480000004172325,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.022299999371170998,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/ground","display_name":"Ground","score":0.7398999929428101},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.67330002784729},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.48339998722076416},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.45239999890327454},{"id":"https://openalex.org/keywords/voting","display_name":"Voting","score":0.37959998846054077},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.35580000281333923}],"concepts":[{"id":"https://openalex.org/C168993435","wikidata":"https://www.wikidata.org/wiki/Q6501125","display_name":"Ground","level":2,"score":0.7398999929428101},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7156999707221985},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.67330002784729},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5443999767303467},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.48339998722076416},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.45239999890327454},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.44110000133514404},{"id":"https://openalex.org/C520049643","wikidata":"https://www.wikidata.org/wiki/Q189760","display_name":"Voting","level":3,"score":0.37959998846054077},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.35580000281333923},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.3287000060081482},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.328000009059906},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3253999948501587},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.3176000118255615},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2867000102996826},{"id":"https://openalex.org/C119898033","wikidata":"https://www.wikidata.org/wiki/Q3433888","display_name":"Ensemble forecasting","level":2,"score":0.27230000495910645},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.26989999413490295}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.00556","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.00556","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2606.00556","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.00556","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"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":{"Visual":[0],"grounding":[1,26,60,80,133,169],"aims":[2],"to":[3,9,31,49,98,112,163],"locate":[4],"image":[5],"regions":[6],"that":[7,71,152],"correspond":[8],"natural":[10],"language":[11],"descriptions":[12],"and":[13,36,66,86,116,144,156,166],"is":[14,27,46,107],"a":[15,43,78,88],"key":[16],"component":[17],"of":[18,76,103],"interpretable":[19],"vision":[20],"systems.":[21],"In":[22,54],"remote":[23,84],"sensing":[24],"imagery,":[25],"particularly":[28],"challenging":[29],"due":[30],"complex":[32],"scenes,":[33],"small":[34],"objects,":[35],"large":[37],"variations":[38],"in":[39],"scale.":[40],"Relying":[41],"on":[42,127],"single":[44],"model":[45,81],"often":[47],"insufficient":[48],"address":[50],"these":[51],"diverse":[52,132],"challenges.":[53],"this":[55],"work,":[56],"we":[57,121],"propose":[58],"two":[59],"pipelines,":[61,134],"Sequential":[62],"Grounding":[63,68],"Refinement":[64,69],"(SGR)":[65],"Cluster-Aware":[67],"(CGR),":[70],"combine":[72],"the":[73,153],"complementary":[74],"strengths":[75],"RemoteSAM,":[77],"visual":[79,168],"specialized":[82],"for":[83],"sensing,":[85],"SAM3,":[87],"powerful":[89],"general-purpose":[90],"segmentation":[91],"model.":[92],"Our":[93],"approach":[94,158],"first":[95],"uses":[96],"RemoteSAM":[97],"obtain":[99],"an":[100,123],"initial":[101],"estimate":[102],"object":[104],"location,":[105],"which":[106],"then":[108],"refined":[109],"using":[110],"SAM3":[111],"produce":[113],"more":[114,164],"accurate":[115],"spatially":[117],"consistent":[118],"segmentations.":[119],"Additionally,":[120],"explore":[122],"ensemble":[124,157],"strategy":[125],"based":[126],"majority":[128],"voting":[129],"across":[130],"six":[131],"each":[135],"with":[136],"distinct":[137],"capabilities.":[138],"This":[139],"multi-model":[140],"framework":[141],"improves":[142],"robustness":[143],"significantly":[145],"enhances":[146],"localization":[147],"accuracy.":[148],"Experimental":[149],"results":[150],"demonstrate":[151],"proposed":[154],"pipelines":[155],"outperform":[159],"individual":[160],"models,":[161],"leading":[162],"reliable":[165],"precise":[167],"predictions.":[170]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-06-03T00:00:00"}
