{"id":"https://openalex.org/W7160951768","doi":"https://doi.org/10.48550/arxiv.2605.09060","title":"Language-Conditioned Visual Grounding with CLIP Multilingual","display_name":"Language-Conditioned Visual Grounding with CLIP Multilingual","publication_year":2026,"publication_date":"2026-05-09","ids":{"openalex":"https://openalex.org/W7160951768","doi":"https://doi.org/10.48550/arxiv.2605.09060"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.09060","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.09060","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.2605.09060","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5135996418","display_name":"J. de Curt\u00f2","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"de Curt\u00f2, J.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130753513","display_name":"Mauro Liz","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liz, Mauro","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5135920906","display_name":"I. de Zarz\u00e0","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"de Zarz\u00e0, I.","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":0.892799973487854,"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":0.892799973487854,"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":0.014600000344216824,"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/T10465","display_name":"Neurobiology of Language and Bilingualism","score":0.009700000286102295,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/ambiguity","display_name":"Ambiguity","score":0.5160999894142151},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.5037999749183655},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.5023000240325928},{"id":"https://openalex.org/keywords/rank","display_name":"Rank (graph theory)","score":0.4828000068664551},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.4361000061035156},{"id":"https://openalex.org/keywords/rank-correlation","display_name":"Rank correlation","score":0.39250001311302185},{"id":"https://openalex.org/keywords/scaling","display_name":"Scaling","score":0.38449999690055847},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.38370001316070557},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.36059999465942383}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6474999785423279},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6003000140190125},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.5479000210762024},{"id":"https://openalex.org/C2780522230","wikidata":"https://www.wikidata.org/wiki/Q1140419","display_name":"Ambiguity","level":2,"score":0.5160999894142151},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.5037999749183655},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.5023000240325928},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.4828000068664551},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.4361000061035156},{"id":"https://openalex.org/C101601086","wikidata":"https://www.wikidata.org/wiki/Q3753228","display_name":"Rank correlation","level":2,"score":0.39250001311302185},{"id":"https://openalex.org/C99844830","wikidata":"https://www.wikidata.org/wiki/Q102441924","display_name":"Scaling","level":2,"score":0.38449999690055847},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.38370001316070557},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3801000118255615},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.36059999465942383},{"id":"https://openalex.org/C112876837","wikidata":"https://www.wikidata.org/wiki/Q837518","display_name":"Alphabet","level":2,"score":0.34380000829696655},{"id":"https://openalex.org/C207390915","wikidata":"https://www.wikidata.org/wiki/Q1230525","display_name":"Divergence (linguistics)","level":2,"score":0.3312000036239624},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.3296000063419342},{"id":"https://openalex.org/C33676613","wikidata":"https://www.wikidata.org/wiki/Q13415176","display_name":"Dimension (graph theory)","level":2,"score":0.3197999894618988},{"id":"https://openalex.org/C42058472","wikidata":"https://www.wikidata.org/wiki/Q810214","display_name":"Base (topology)","level":2,"score":0.31279999017715454},{"id":"https://openalex.org/C16345878","wikidata":"https://www.wikidata.org/wiki/Q107472979","display_name":"Orientation (vector space)","level":2,"score":0.30720001459121704},{"id":"https://openalex.org/C117220453","wikidata":"https://www.wikidata.org/wiki/Q5172842","display_name":"Correlation","level":2,"score":0.2825999855995178},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.2782000005245209},{"id":"https://openalex.org/C91682802","wikidata":"https://www.wikidata.org/wiki/Q620538","display_name":"Multidimensional scaling","level":2,"score":0.27790001034736633},{"id":"https://openalex.org/C2777508537","wikidata":"https://www.wikidata.org/wiki/Q7936620","display_name":"Visual reasoning","level":2,"score":0.2736000120639801},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.27000001072883606},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.2687000036239624},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.25440001487731934},{"id":"https://openalex.org/C143271835","wikidata":"https://www.wikidata.org/wiki/Q254515","display_name":"Similitude","level":2,"score":0.25290000438690186},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.2522999942302704}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.09060","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.09060","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.2605.09060","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.09060","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":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Multilingual":[0],"vision-language":[1],"models":[2],"exhibit":[3],"systematic":[4],"performance":[5],"gaps":[6],"across":[7,46,175],"languages,":[8],"but":[9],"the":[10,19,22,40,53,139,142,147,151,195],"mechanism":[11],"remains":[12],"ambiguous:":[13],"cross-language":[14,89],"divergence":[15],"could":[16],"arise":[17],"from":[18,167],"visual":[20,41,74],"encoder,":[21],"text":[23,55,143],"branch,":[24],"or":[25],"their":[26],"interaction.":[27],"We":[28,58],"resolve":[29],"this":[30],"ambiguity":[31],"through":[32],"a":[33,64,119,216],"dense":[34],"multilingual":[35,221],"CLIP":[36,61],"probe":[37],"in":[38],"which":[39],"encoder":[42,148],"is":[43,173,207],"held":[44],"identical":[45],"thirteen":[47],"typologically":[48],"diverse":[49],"languages":[50,114,176],"and":[51,84,87,96],"only":[52],"XLM-RoBERTa":[54],"branch":[56],"varies.":[57],"evaluate":[59],"two":[60],"architectures":[62],"spanning":[63],"7x":[65,149],"visual-encoder":[66],"scale":[67],"gap":[68,131,152],"(XLM-R":[69],"base":[70],"+":[71,78],"ViT-B/32,":[72],"~87M":[73],"parameters;":[75],"XLM-R":[76],"large":[77,181],"ViT-H/14,":[79],"~632M)":[80],"on":[81],"11":[82],"concepts":[83],"210":[85],"images,":[86],"quantify":[88],"agreement":[90],"via":[91],"cluster-mask":[92,129,184],"IoU,":[93,95],"top-percentile":[94],"Spearman":[97],"rank":[98],"correlation":[99],"against":[100],"an":[101],"English":[102],"reference":[103],"(n=2,310":[104],"paired":[105],"observations":[106],"per":[107,202],"language).":[108],"Three":[109],"findings":[110],"emerge.":[111],"First,":[112],"low-resource":[113],"(Arabic,":[115],"Basque,":[116],"Luxembourgish)":[117],"incur":[118],"structural":[120,154],"penalty":[121],"at":[122,133,136,180],"both":[123],"backbone":[124],"scales":[125],"(Wilcoxon":[126],"HR>LR":[127],"p<10^-300;":[128],"IoU":[130,185],"+0.114":[132],"base,":[134],"+0.143":[135],"large),":[137],"isolating":[138],"deficit":[140],"to":[141],"branch.":[144],"Second,":[145],"scaling":[146],"widens":[150],"for":[153,219],"failure":[155,197],"cases":[156],"(Basque":[157],"\u0394=-0.056,":[158],"Luxembourgish":[159],"\u0394=-0.076)":[160],"while":[161,183],"improving":[162],"Arabic":[163],"(\u0394=+0.033),":[164],"separating":[165],"corpus-coverage":[166],"tokeniser-fertility":[168],"failures.":[169],"Third,":[170],"peak":[171],"similarity":[172],"preserved":[174],"(mean":[177],"ratio":[178],"0.94":[179],"scale)":[182],"drops":[186],"sharply,":[187],"identifying":[188],"spatial":[189],"misalignment,":[190],"not":[191],"signal":[192],"collapse,":[193],"as":[194,215],"dominant":[196],"mode.":[198],"At":[199],"3.4-3.9":[200],"Wh":[201],"1,000":[203],"queries,":[204],"dense-CLIP":[205],"grounding":[206],"competitive":[208],"with":[209],"high-throughput":[210],"inference":[211],"budgets,":[212],"positioning":[213],"it":[214],"practical":[217],"substrate":[218],"energy-aware":[220],"deployment.":[222]},"counts_by_year":[],"updated_date":"2026-08-21T09:56:20.448147","created_date":"2026-05-13T00:00:00"}