{"id":"https://openalex.org/W7154453231","doi":"https://doi.org/10.48550/arxiv.2604.12159","title":"VidTAG: Temporally Aligned Video to GPS Geolocalization with Denoising Sequence Prediction at a Global Scale","display_name":"VidTAG: Temporally Aligned Video to GPS Geolocalization with Denoising Sequence Prediction at a Global Scale","publication_year":2026,"publication_date":"2026-04-14","ids":{"openalex":"https://openalex.org/W7154453231","doi":"https://doi.org/10.48550/arxiv.2604.12159"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.12159","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.12159","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":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.2604.12159","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5133633625","display_name":"Parth Parag Kulkarni","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kulkarni, Parth Parag","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133639457","display_name":"Rohit Gupta","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gupta, Rohit","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5035834705","display_name":"Prakash Chandra Chhipa","orcid":"https://orcid.org/0000-0002-6903-7552"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chhipa, Prakash Chandra","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5133635069","display_name":"Mubarak Shah","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shah, Mubarak","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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.24740000069141388,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.24740000069141388,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.2094999998807907,"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/T12357","display_name":"Digital Media Forensic Detection","score":0.11349999904632568,"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/global-positioning-system","display_name":"Global Positioning System","score":0.7706000208854675},{"id":"https://openalex.org/keywords/granularity","display_name":"Granularity","score":0.5684999823570251},{"id":"https://openalex.org/keywords/frame","display_name":"Frame (networking)","score":0.4722000062465668},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.44920000433921814},{"id":"https://openalex.org/keywords/reference-frame","display_name":"Reference frame","score":0.4350999891757965},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.3959999978542328},{"id":"https://openalex.org/keywords/sequence","display_name":"Sequence (biology)","score":0.3393999934196472}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7767000198364258},{"id":"https://openalex.org/C60229501","wikidata":"https://www.wikidata.org/wiki/Q18822","display_name":"Global Positioning System","level":2,"score":0.7706000208854675},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5730999708175659},{"id":"https://openalex.org/C177774035","wikidata":"https://www.wikidata.org/wiki/Q1246948","display_name":"Granularity","level":2,"score":0.5684999823570251},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5440999865531921},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.4722000062465668},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.44920000433921814},{"id":"https://openalex.org/C172849965","wikidata":"https://www.wikidata.org/wiki/Q3148875","display_name":"Reference frame","level":3,"score":0.4350999891757965},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.3959999978542328},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.3393999934196472},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3181999921798706},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3149999976158142},{"id":"https://openalex.org/C30814859","wikidata":"https://www.wikidata.org/wiki/Q4119603","display_name":"Video denoising","level":5,"score":0.30399999022483826},{"id":"https://openalex.org/C2983174267","wikidata":"https://www.wikidata.org/wiki/Q3775098","display_name":"Video retrieval","level":2,"score":0.28999999165534973},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.2752000093460083},{"id":"https://openalex.org/C123046963","wikidata":"https://www.wikidata.org/wiki/Q22664","display_name":"Geographic coordinate system","level":2,"score":0.2646999955177307},{"id":"https://openalex.org/C53605480","wikidata":"https://www.wikidata.org/wiki/Q852595","display_name":"Geotagging","level":2,"score":0.26080000400543213},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.2581000030040741},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.2540000081062317}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.12159","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.12159","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":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.2604.12159","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.12159","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11","score":0.7929942011833191}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"The":[0],"task":[1],"of":[2,12,70],"video":[3,98,166,173],"geolocalization":[4,167,174],"aims":[5],"to":[6,39,54,64,136],"determine":[7],"the":[8,55,102,110,121,149,186],"precise":[9],"GPS":[10,71,118],"coordinates":[11,72],"a":[13,33,50,68,80,145,177],"video's":[14],"origin":[15],"and":[16,26,37,75,90,109,129,141,175],"map":[17],"its":[18],"trajectory;":[19],"with":[20],"applications":[21],"in":[22,97],"forensics,":[23],"social":[24],"media,":[25],"exploration.":[27],"Existing":[28],"classification-based":[29],"approaches":[30],"operate":[31],"at":[32,148],"coarse":[34,164],"city-level":[35],"granularity":[36],"fail":[38],"capture":[40],"fine-grained":[41,172],"details,":[42],"while":[43],"image":[44,59],"retrieval":[45,86],"methods":[46],"are":[47,62],"impractical":[48],"on":[49,126,162,185],"global":[51,163],"scale":[52],"due":[53],"need":[56],"for":[57,180],"extensive":[58],"galleries":[60],"which":[61,105],"infeasible":[63],"compile.":[65],"Comparatively,":[66],"constructing":[67],"gallery":[69],"is":[73],"straightforward":[74],"inexpensive.":[76],"We":[77,155],"propose":[78],"VidTAG,":[79],"dual-encoder":[81],"framework":[82],"that":[83,116],"performs":[84],"frame-to-GPS":[85],"using":[87,120],"both":[88],"self-supervised":[89],"language-aligned":[91],"features.":[92],"To":[93],"address":[94],"temporal":[95],"inconsistencies":[96],"predictions,":[99],"we":[100],"introduce":[101],"TempGeo":[103],"module,":[104,112],"aligns":[106],"frame":[107,123],"embeddings,":[108],"GeoRefiner":[111],"an":[113],"encoder-decoder":[114],"architecture":[115],"refines":[117],"features":[119],"aligned":[122],"embeddings.":[124],"Evaluations":[125],"Mapillary":[127],"(MSLS)":[128],"GAMa":[130],"datasets":[131],"demonstrate":[132],"our":[133],"model's":[134],"ability":[135],"generate":[137],"temporally":[138],"consistent":[139],"trajectories":[140],"outperform":[142],"baselines,":[143],"achieving":[144],"20%":[146],"improvement":[147],"1":[150],"km":[151],"threshold":[152],"over":[153],"GeoCLIP.":[154],"also":[156],"beat":[157],"current":[158],"State-of-the-Art":[159],"by":[160],"25%":[161],"grained":[165],"(CityGuessr68k).":[168],"Our":[169],"approach":[170],"enables":[171],"lays":[176],"strong":[178],"foundation":[179],"future":[181],"research.":[182],"More":[183],"details":[184],"project":[187],"webpage:":[188],"https://parthpk.github.io/vidtag_webpage/":[189]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-04-16T00:00:00"}