{"id":"https://openalex.org/W7163676763","doi":"https://doi.org/10.48550/arxiv.2606.06338","title":"StoryVideoQA: Scaling Deep Video Understanding with a Large-Scale, Multi-Genre and Auto-Generated Dataset","display_name":"StoryVideoQA: Scaling Deep Video Understanding with a Large-Scale, Multi-Genre and Auto-Generated Dataset","publication_year":2026,"publication_date":"2026-06-04","ids":{"openalex":"https://openalex.org/W7163676763","doi":"https://doi.org/10.48550/arxiv.2606.06338"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.06338","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.06338","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":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2606.06338","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5011464051","display_name":"Zhengqian Wu","orcid":"https://orcid.org/0009-0002-4258-9581"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wu, Zhengqian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137920599","display_name":"Zhixian Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Zhixian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137908623","display_name":"Aodong Chen","orcid":"https://orcid.org/0009-0005-0802-1599"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Aodong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137938631","display_name":"Jingyang Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Jingyang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137961406","display_name":"Ruizhe Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Ruizhe","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137822898","display_name":"Hanlin Ge","orcid":"https://orcid.org/0009-0001-8976-5194"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ge, Hanlin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137998474","display_name":"Zhongyuan Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Zhongyuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100768160","display_name":"Chunxia Xiao","orcid":"https://orcid.org/0000-0002-4526-6297"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xiao, Chunxia","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5138009486","display_name":"Chao Liang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liang, Chao","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":0.958299994468689,"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":0.958299994468689,"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/T10028","display_name":"Topic Modeling","score":0.01759999990463257,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.00419999985024333,"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/construct","display_name":"Construct (python library)","score":0.8062999844551086},{"id":"https://openalex.org/keywords/bridge","display_name":"Bridge (graph theory)","score":0.49149999022483826},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.48339998722076416},{"id":"https://openalex.org/keywords/voting","display_name":"Voting","score":0.44350001215934753},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.43050000071525574},{"id":"https://openalex.org/keywords/comprehension","display_name":"Comprehension","score":0.41609999537467957},{"id":"https://openalex.org/keywords/question-answering","display_name":"Question answering","score":0.4104999899864197},{"id":"https://openalex.org/keywords/mode","display_name":"Mode (computer interface)","score":0.35030001401901245}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8482000231742859},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.8062999844551086},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5335999727249146},{"id":"https://openalex.org/C100776233","wikidata":"https://www.wikidata.org/wiki/Q2532492","display_name":"Bridge (graph theory)","level":2,"score":0.49149999022483826},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.48339998722076416},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.44519999623298645},{"id":"https://openalex.org/C520049643","wikidata":"https://www.wikidata.org/wiki/Q189760","display_name":"Voting","level":3,"score":0.44350001215934753},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.43050000071525574},{"id":"https://openalex.org/C511192102","wikidata":"https://www.wikidata.org/wiki/Q5156948","display_name":"Comprehension","level":2,"score":0.41609999537467957},{"id":"https://openalex.org/C44291984","wikidata":"https://www.wikidata.org/wiki/Q1074173","display_name":"Question answering","level":2,"score":0.4104999899864197},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3549000024795532},{"id":"https://openalex.org/C48677424","wikidata":"https://www.wikidata.org/wiki/Q6888088","display_name":"Mode (computer interface)","level":2,"score":0.35030001401901245},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.335999995470047},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.33149999380111694},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.3163999915122986},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.31150001287460327},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.2957000136375427},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2872999906539917},{"id":"https://openalex.org/C167651023","wikidata":"https://www.wikidata.org/wiki/Q1474611","display_name":"Plot (graphics)","level":2,"score":0.28139999508857727},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.2754000127315521},{"id":"https://openalex.org/C157657479","wikidata":"https://www.wikidata.org/wiki/Q2367247","display_name":"Closed captioning","level":3,"score":0.2721000015735626},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.26919999718666077},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.26589998602867126},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2596000134944916},{"id":"https://openalex.org/C2781316041","wikidata":"https://www.wikidata.org/wiki/Q1230584","display_name":"Diversity (politics)","level":2,"score":0.2574999928474426},{"id":"https://openalex.org/C2780861071","wikidata":"https://www.wikidata.org/wiki/Q1062934","display_name":"Character (mathematics)","level":2,"score":0.25600001215934753},{"id":"https://openalex.org/C2983174267","wikidata":"https://www.wikidata.org/wiki/Q3775098","display_name":"Video retrieval","level":2,"score":0.25529998540878296}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.06338","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.06338","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":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2606.06338","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.06338","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":"Preprint"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions","score":0.4239392578601837}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Video":[0],"question":[1,42],"answering":[2],"(VideoQA)":[3],"aims":[4],"to":[5,79,123,151,158],"answer":[6],"questions":[7],"about":[8],"given":[9],"videos.":[10],"While":[11],"existing":[12],"approaches":[13],"excel":[14],"on":[15,164,188,234],"factoid":[16],"VideoQA,":[17],"they":[18,194],"struggle":[19],"with":[20,84],"deep":[21],"video":[22,39,218,223],"understanding":[23,205,219],"(DVU),":[24],"which":[25,50],"requires":[26],"the":[27,36,52,64,147,154],"comprehension":[28],"of":[29,49,56,68,183,206],"complex":[30,109,207],"storylines.":[31,208],"This":[32],"challenge":[33],"arises":[34],"from":[35],"inherent":[37],"long-range":[38,198,222],"content,":[40],"multi-faceted":[41],"types,":[43],"and":[44,54,66,107,132,141,176],"instance-level":[45],"story":[46,168],"elements,":[47],"all":[48],"constrain":[51,63],"scale":[53,65],"diversity":[55,67],"manually":[57],"constructed":[58],"DVU":[59,70,82,126,156],"datasets.":[60],"These":[61],"difficulties":[62],"manually-constructed":[69],"dataset.":[71],"To":[72,209],"address":[73],"these,":[74],"we":[75,114,213],"previously":[76],"introduced":[77],"StoryMind":[78],"automatically":[80],"construct":[81,152,202],"datasets":[83,127],"balanced":[85],"fine-grained":[86],"topics.":[87],"Though":[88],"it":[89,99],"can":[90],"generate":[91,124],"high-quality":[92,125],"question-answer":[93],"pairs":[94],"(QAs)":[95],"for":[96,128],"TV":[97,130,171],"series,":[98],"suffers":[100],"significant":[101],"performance":[102],"degradation":[103],"when":[104],"handling":[105],"longer":[106],"more":[108],"movies.":[110,133],"In":[111],"this":[112,189,211],"paper,":[113],"further":[115],"design":[116],"StoryMindv2,":[117],"an":[118],"enhanced":[119],"multi-agent":[120],"collaboration":[121],"framework":[122,148],"both":[129],"series":[131,172],"By":[134],"integrating":[135],"a":[136,142,203,216,226],"novel":[137,217],"supervisor-guided":[138],"generation":[139],"mechanism":[140],"refined":[143],"multi-reviewer":[144],"voting":[145],"strategy,":[146],"is":[149],"utilized":[150],"StoryVideoQA,":[153],"largest":[155],"dataset":[157],"date,":[159],"featuring":[160],"over":[161],"363K":[162],"QAs":[163],"393.2":[165],"hours":[166],"diverse":[167],"videos":[169],"including":[170],"(avg.":[173,178],"1,635":[174],"seconds)":[175],"movies":[177],"7,878":[179],"seconds).":[180],"Comprehensive":[181],"evaluations":[182],"20":[184],"state-of-the-art":[185],"VideoQA":[186],"methods":[187],"large-scale":[190],"benchmark":[191],"reveal":[192],"that":[193],"cannot":[195],"fully":[196],"maintain":[197],"character":[199],"associations":[200],"or":[201],"coherent":[204],"bridge":[210],"gap,":[212],"propose":[214],"PlotTree,":[215],"agent,":[220],"re-organizing":[221],"content":[224],"into":[225],"hierarchical":[227],"plot":[228],"structure,":[229],"enabling":[230],"efficient":[231],"storyline":[232],"reasoning":[233],"StoryVideoQA.":[235],"Project":[236],"page:":[237],"https://github.com/nercms-mmap/StoryVideoQA/":[238]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-06T00:00:00"}