{"id":"https://openalex.org/W7167612917","doi":"https://doi.org/10.48550/arxiv.2607.02963","title":"Parallelized Autoregressive Decoding for Omni-Modal Dense Video Captioning","display_name":"Parallelized Autoregressive Decoding for Omni-Modal Dense Video Captioning","publication_year":2026,"publication_date":"2026-07-03","ids":{"openalex":"https://openalex.org/W7167612917","doi":"https://doi.org/10.48550/arxiv.2607.02963"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2607.02963","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.02963","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.2607.02963","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5008482043","display_name":"Wenzheng Zeng","orcid":"https://orcid.org/0000-0001-8467-5862"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zeng, Wenzheng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5114334915","display_name":"Siyi Jiao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jiao, Siyi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5140215393","display_name":"Chen Gao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gao, Chen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5110081955","display_name":"Hwee Tou Ng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ng, Hwee Tou","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5140205427","display_name":"Mike Zheng Shou","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shou, Mike Zheng","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":0.979200005531311,"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":0.979200005531311,"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":0.009800000116229057,"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":0.001500000013038516,"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/closed-captioning","display_name":"Closed captioning","score":0.8008000254631042},{"id":"https://openalex.org/keywords/decoding-methods","display_name":"Decoding methods","score":0.6786999702453613},{"id":"https://openalex.org/keywords/lossless-compression","display_name":"Lossless compression","score":0.5217000246047974},{"id":"https://openalex.org/keywords/event","display_name":"Event (particle physics)","score":0.5134999752044678},{"id":"https://openalex.org/keywords/dependency","display_name":"Dependency (UML)","score":0.4900999963283539},{"id":"https://openalex.org/keywords/encoding","display_name":"Encoding (memory)","score":0.48820000886917114},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.48660001158714294},{"id":"https://openalex.org/keywords/autoregressive-model","display_name":"Autoregressive model","score":0.48159998655319214},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.47279998660087585}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8345999717712402},{"id":"https://openalex.org/C157657479","wikidata":"https://www.wikidata.org/wiki/Q2367247","display_name":"Closed captioning","level":3,"score":0.8008000254631042},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.6786999702453613},{"id":"https://openalex.org/C81081738","wikidata":"https://www.wikidata.org/wiki/Q55542","display_name":"Lossless compression","level":3,"score":0.5217000246047974},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.5134999752044678},{"id":"https://openalex.org/C19768560","wikidata":"https://www.wikidata.org/wiki/Q320727","display_name":"Dependency (UML)","level":2,"score":0.4900999963283539},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.48820000886917114},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.48660001158714294},{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.48159998655319214},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.47279998660087585},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4521999955177307},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.38830000162124634},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.3837999999523163},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.3668000102043152},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.3571000099182129},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.3450999855995178},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.32679998874664307},{"id":"https://openalex.org/C189645446","wikidata":"https://www.wikidata.org/wiki/Q350865","display_name":"Mirroring","level":2,"score":0.31380000710487366},{"id":"https://openalex.org/C64357122","wikidata":"https://www.wikidata.org/wiki/Q1149766","display_name":"Causality (physics)","level":2,"score":0.31049999594688416},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.29600000381469727},{"id":"https://openalex.org/C112313634","wikidata":"https://www.wikidata.org/wiki/Q7886648","display_name":"Complement (music)","level":5,"score":0.2930000126361847},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.28859999775886536},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.2863999903202057},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.2833999991416931},{"id":"https://openalex.org/C160145156","wikidata":"https://www.wikidata.org/wiki/Q778586","display_name":"Executable","level":2,"score":0.28189998865127563},{"id":"https://openalex.org/C48145219","wikidata":"https://www.wikidata.org/wiki/Q1335365","display_name":"Security token","level":2,"score":0.26510000228881836},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2648000121116638},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.25839999318122864},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.2506999969482422}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2607.02963","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.02963","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.2607.02963","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.02963","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":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Dense":[0],"video":[1,10,15,23,58],"captioning":[2,84],"aims":[3],"to":[4,34,90,100,132],"generate":[5],"temporally":[6,82,97],"grounded":[7,83],"descriptions":[8],"of":[9,207],"events,":[11],"benefiting":[12],"both":[13,211],"event-level":[14,160,173],"understanding":[16],"and":[17,38,54,60,162,180,183,213,219],"generation.":[18,110],"In":[19,64],"this":[20,65,139],"domain,":[21],"autoregressive":[22,71],"large":[24],"language":[25],"models":[26],"have":[27],"emerged":[28],"as":[29,57],"a":[30,69,151],"prevalent":[31],"paradigm":[32,49],"due":[33],"their":[35],"strong":[36],"generative":[37],"cross-modal":[39],"modeling":[40],"capacity.":[41],"However,":[42],"generating":[43],"dense":[44],"captions":[45],"under":[46],"the":[47,92,102,159,204],"token-by-token":[48],"severely":[50],"limits":[51],"inference":[52],"efficiency":[53,78,212],"hinders":[55],"scalability":[56],"length":[59],"event":[61,128,217],"density":[62],"increase.":[63],"work,":[66],"we":[67,141],"propose":[68],"parallelized":[70],"framework":[72],"that":[73,156,190],"not":[74],"only":[75],"improves":[76],"generation":[77],"but":[79],"also":[80],"enhances":[81],"performance.":[85],"Our":[86],"key":[87,144],"insight":[88],"is":[89],"exploit":[91],"weak":[93,114],"local":[94,134,193],"dependencies":[95,116],"across":[96],"distinct":[98],"events":[99],"restructure":[101],"causal":[103],"dependency":[104,178],"graph,":[105],"thereby":[106],"enabling":[107],"lossless":[108,147],"parallel":[109,148,181,187],"Specifically,":[111],"tokens":[112,125,165],"with":[113,195],"cross-event":[115],"can":[117],"be":[118],"decoded":[119],"in":[120,210,215],"parallel,":[121],"while":[122,170],"tightly":[123],"coupled":[124],"within":[126],"each":[127],"retain":[129],"sequential":[130],"decoding":[131,188],"preserve":[133],"semantic":[135],"coherence.":[136],"To":[137],"realize":[138],"insight,":[140],"introduce":[142],"two":[143],"components":[145],"for":[146],"decoding:":[149],"(1)":[150],"latent":[152],"global":[153,167,196],"planning":[154],"mechanism":[155,189],"automatically":[157],"learns":[158],"structure":[161],"produces":[163],"compact":[164],"encoding":[166],"inter-event":[168,197],"causality":[169],"adaptively":[171],"aggregating":[172],"audio-visual":[174],"semantics,":[175],"guiding":[176],"subsequent":[177],"restructuring":[179],"decoding;":[182],"(2)":[184],"an":[185],"event-factorized":[186],"effectively":[191],"balances":[192],"focus":[194],"awareness.":[198],"Experiments":[199],"on":[200],"various":[201],"benchmarks":[202],"demonstrate":[203],"clear":[205],"advantage":[206],"our":[208],"approach":[209],"performance":[214],"omni-modal":[216],"grounding":[218],"captioning.":[220],"Project":[221],"website:":[222],"https://github.com/showlab/PadCaptioner.":[223]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-07-08T00:00:00"}