{"id":"https://openalex.org/W7154333996","doi":"https://doi.org/10.48550/arxiv.2604.10385","title":"GTASA: Ground Truth Annotations for Spatiotemporal Analysis, Evaluation and Training of Video Models","display_name":"GTASA: Ground Truth Annotations for Spatiotemporal Analysis, Evaluation and Training of Video Models","publication_year":2026,"publication_date":"2026-04-12","ids":{"openalex":"https://openalex.org/W7154333996","doi":"https://doi.org/10.48550/arxiv.2604.10385"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.10385","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.10385","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.2604.10385","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5016317974","display_name":"Nicolae Cudlenco","orcid":"https://orcid.org/0000-0001-6547-3659"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cudlenco, Nicolae","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5075638103","display_name":"Mihai Masala","orcid":"https://orcid.org/0000-0003-3496-9058"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Masala, Mihai","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5086392501","display_name":"Marius Leordeanu","orcid":"https://orcid.org/0000-0001-8479-8758"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Leordeanu, Marius","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":0.964900016784668,"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":0.964900016784668,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.008100000210106373,"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/T10812","display_name":"Human Pose and Action Recognition","score":0.004000000189989805,"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/ground-truth","display_name":"Ground truth","score":0.788100004196167},{"id":"https://openalex.org/keywords/closed-captioning","display_name":"Closed captioning","score":0.7186999917030334},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.6837999820709229},{"id":"https://openalex.org/keywords/encode","display_name":"ENCODE","score":0.6341999769210815},{"id":"https://openalex.org/keywords/relation","display_name":"Relation (database)","score":0.5101000070571899},{"id":"https://openalex.org/keywords/spatial-relation","display_name":"Spatial relation","score":0.44589999318122864},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.43630000948905945},{"id":"https://openalex.org/keywords/visualization","display_name":"Visualization","score":0.42500001192092896}],"concepts":[{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.788100004196167},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7784000039100647},{"id":"https://openalex.org/C157657479","wikidata":"https://www.wikidata.org/wiki/Q2367247","display_name":"Closed captioning","level":3,"score":0.7186999917030334},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.6837999820709229},{"id":"https://openalex.org/C66746571","wikidata":"https://www.wikidata.org/wiki/Q1134833","display_name":"ENCODE","level":3,"score":0.6341999769210815},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5694000124931335},{"id":"https://openalex.org/C25343380","wikidata":"https://www.wikidata.org/wiki/Q277521","display_name":"Relation (database)","level":2,"score":0.5101000070571899},{"id":"https://openalex.org/C27511587","wikidata":"https://www.wikidata.org/wiki/Q2178623","display_name":"Spatial relation","level":2,"score":0.44589999318122864},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.43630000948905945},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.42500001192092896},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.4196000099182129},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.4088999927043915},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.38989999890327454},{"id":"https://openalex.org/C155911833","wikidata":"https://www.wikidata.org/wiki/Q3817354","display_name":"Spatial intelligence","level":2,"score":0.36739999055862427},{"id":"https://openalex.org/C179372163","wikidata":"https://www.wikidata.org/wiki/Q1406181","display_name":"Scene graph","level":3,"score":0.3635999858379364},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.36039999127388},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.35830000042915344},{"id":"https://openalex.org/C2778572836","wikidata":"https://www.wikidata.org/wiki/Q380933","display_name":"Space (punctuation)","level":2,"score":0.34700000286102295},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.33390000462532043},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.33230000734329224},{"id":"https://openalex.org/C197914299","wikidata":"https://www.wikidata.org/wiki/Q18650","display_name":"Semantic memory","level":3,"score":0.31929999589920044},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.30410000681877136},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.2833000123500824},{"id":"https://openalex.org/C2777508537","wikidata":"https://www.wikidata.org/wiki/Q7936620","display_name":"Visual reasoning","level":2,"score":0.25110000371932983}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.10385","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.10385","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.2604.10385","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.10385","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":[{"display_name":"Peace, Justice and strong institutions","score":0.4716816544532776,"id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Game":[0],"engines":[1],"hold":[2],"what":[3,87],"video":[4,105,155,160,181],"models":[5],"struggle":[6],"to":[7,96],"learn:":[8],"a":[9,21,83,107,173,206],"complete,":[10],"explicit":[11],"world":[12],"state":[13,119],"behind":[14],"every":[15,113],"frame.":[16],"We":[17,124,194],"turn":[18],"one":[19],"into":[20,46],"data":[22],"instrument.":[23],"GEST-Engine,":[24],"our":[25,97],"production-grade":[26],"open-source":[27],"system,":[28],"deterministically":[29],"executes":[30],"Graphs":[31],"of":[32,48,86,103,120,132,179,192],"Events":[33],"in":[34],"Space":[35],"and":[36,60,70,135,148,200],"Time":[37],"(GESTs),":[38],"whether":[39],"procedurally":[40],"generated":[41],"or":[42],"derived":[43],"from":[44],"text,":[45],"videos":[47],"synchronized":[49],"multi-actor":[50],"scenarios,":[51],"recording":[52],"ground":[53,171],"truth":[54],"as":[55],"it":[56,79],"renders:":[57],"3D":[58,170],"entity":[59],"camera":[61],"state,":[62],"pairwise":[63],"spatial":[64],"relations,":[65],"event-to-frame":[66],"mappings,":[67],"instance":[68],"segmentation,":[69],"long":[71],"descriptions,":[72],"at":[73,92,112],"zero":[74],"marginal":[75],"annotation":[76],"cost.":[77],"With":[78],"we":[80],"release":[81,195],"GTASA,":[82],"938-video":[84],"sample":[85],"the":[88,99,118,121,143,196,198,201],"system":[89],"can":[90],"generate":[91],"arbitrary":[93],"scale,":[94],"carrying,":[95],"knowledge,":[98],"densest":[100],"spatial-relation":[101],"coverage":[102],"any":[104],"dataset:":[106],"complete":[108],"entity-pair":[109],"relation":[110],"graph":[111],"frame,":[114],"~84x":[115],"denser":[116],"than":[117],"art,":[122],"frame-for-frame.":[123],"validate":[125],"GTASA":[126,151],"both":[127],"qualitatively,":[128],"through":[129],"human":[130],"evaluation":[131],"physical":[133],"validity":[134],"semantic":[136],"alignment":[137],"where":[138],"frontier":[139],"neural":[140],"generators,":[141],"given":[142],"same":[144],"prompts,":[145],"largely":[146],"fail,":[147],"quantitatively,":[149],"with":[150],"pretraining":[152],"improving":[153],"VLM":[154],"captioning.":[156],"Probing":[157],"six":[158],"frozen":[159,180],"encoders":[161],"across":[162],"11":[163],"spatio-temporal":[164],"tasks":[165],"enabled":[166],"by":[167],"GTASA's":[168],"exact":[169],"truth,":[172],"previously":[174],"untestable":[175],"inter-entity":[176],"relational":[177],"probe":[178],"features,":[182],"reveals":[183],"that":[184],"who-is-near-whom":[185],"barely":[186],"rises":[187],"above":[188],"chance":[189],"for":[190],"all":[191],"them.":[193],"engine,":[197],"corpus,":[199],"benchmark,":[202],"making":[203],"this":[204],"gap":[205],"measurable,":[207],"trainable":[208],"target.":[209]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-04-15T00:00:00"}