{"id":"https://openalex.org/W4415248402","doi":"https://doi.org/10.48550/arxiv.2505.03581","title":"DyGEnc: Encoding a Sequence of Textual Scene Graphs to Reason and Answer Questions in Dynamic Scenes","display_name":"DyGEnc: Encoding a Sequence of Textual Scene Graphs to Reason and Answer Questions in Dynamic Scenes","publication_year":2025,"publication_date":"2025-05-06","ids":{"openalex":"https://openalex.org/W4415248402","doi":"https://doi.org/10.48550/arxiv.2505.03581"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2505.03581","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2505.03581","pdf_url":"https://arxiv.org/pdf/2505.03581","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2505.03581","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5076620129","display_name":"Sergey Linok","orcid":"https://orcid.org/0000-0002-8288-8335"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Linok, Sergey","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5080405508","display_name":"V. A. Semenov","orcid":"https://orcid.org/0000-0002-8766-8454"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Semenov, Vadim","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5120024133","display_name":"Anastasia Trunova","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Trunova, Anastasia","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5026662572","display_name":"Oleg Bulichev","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bulichev, Oleg","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5043502396","display_name":"Dmitry Yudin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yudin, Dmitry","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5076620129"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"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/T10028","display_name":"Topic Modeling","score":0.9987000226974487,"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/T10028","display_name":"Topic Modeling","score":0.9987000226974487,"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/T10181","display_name":"Natural Language Processing Techniques","score":0.9954000115394592,"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/T13083","display_name":"Advanced Text Analysis Techniques","score":0.9919999837875366,"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/encoding","display_name":"Encoding (memory)","score":0.7559999823570251},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.6592000126838684},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.6154000163078308},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.613099992275238},{"id":"https://openalex.org/keywords/sequence","display_name":"Sequence (biology)","score":0.5997999906539917},{"id":"https://openalex.org/keywords/margin","display_name":"Margin (machine learning)","score":0.5400999784469604},{"id":"https://openalex.org/keywords/question-answering","display_name":"Question answering","score":0.5030999779701233},{"id":"https://openalex.org/keywords/robot","display_name":"Robot","score":0.474700003862381},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.40389999747276306}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8210999965667725},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.7559999823570251},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.6592000126838684},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6389999985694885},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.6154000163078308},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.613099992275238},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.5997999906539917},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.5400999784469604},{"id":"https://openalex.org/C44291984","wikidata":"https://www.wikidata.org/wiki/Q1074173","display_name":"Question answering","level":2,"score":0.5030999779701233},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.474700003862381},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.40389999747276306},{"id":"https://openalex.org/C2777508537","wikidata":"https://www.wikidata.org/wiki/Q7936620","display_name":"Visual reasoning","level":2,"score":0.39910000562667847},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.3781999945640564},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3725999891757965},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.35839998722076416},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.35339999198913574},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.34310001134872437},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.3296000063419342},{"id":"https://openalex.org/C64876066","wikidata":"https://www.wikidata.org/wiki/Q5141226","display_name":"Cognitive neuroscience of visual object recognition","level":3,"score":0.3240000009536743},{"id":"https://openalex.org/C40506919","wikidata":"https://www.wikidata.org/wiki/Q7452469","display_name":"Sequence learning","level":2,"score":0.29339998960494995},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.2773999869823456},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.27549999952316284},{"id":"https://openalex.org/C169900460","wikidata":"https://www.wikidata.org/wiki/Q2200417","display_name":"Cognition","level":2,"score":0.26409998536109924},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.2606000006198883},{"id":"https://openalex.org/C161301231","wikidata":"https://www.wikidata.org/wiki/Q3478658","display_name":"Knowledge representation and reasoning","level":2,"score":0.25999999046325684},{"id":"https://openalex.org/C78548338","wikidata":"https://www.wikidata.org/wiki/Q2493","display_name":"Data compression","level":2,"score":0.25619998574256897},{"id":"https://openalex.org/C34413123","wikidata":"https://www.wikidata.org/wiki/Q170978","display_name":"Robotics","level":3,"score":0.25220000743865967},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.25119999051094055}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2505.03581","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2505.03581","pdf_url":"https://arxiv.org/pdf/2505.03581","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2505.03581","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2505.03581","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2505.03581","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2505.03581","pdf_url":"https://arxiv.org/pdf/2505.03581","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4415248402.pdf","grobid_xml":"https://content.openalex.org/works/W4415248402.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"The":[0,76],"analysis":[1],"of":[2,14,20,72,78,91,114,122,152,172],"events":[3],"in":[4,11,116],"dynamic":[5],"environments":[6],"poses":[7],"a":[8,52,57,89,111,153,158],"fundamental":[9],"challenge":[10],"the":[12,69,98,120,126,150,170],"development":[13],"intelligent":[15],"agents":[16],"and":[17,100,174],"robots":[18],"capable":[19],"interacting":[21],"with":[22,68,157],"humans.":[23],"Current":[24],"approaches":[25],"predominantly":[26],"utilize":[27],"visual":[28,108],"models.":[29,75],"However,":[30],"these":[31,165],"methods":[32,109],"often":[33],"capture":[34],"information":[35],"implicitly":[36],"from":[37],"images,":[38],"lacking":[39],"interpretable":[40],"spatial-temporal":[41,64],"object":[42],"representations.":[43],"To":[44],"address":[45],"this":[46,79],"issue":[47],"we":[48],"introduce":[49],"DyGEnc":[50,105],"-":[51],"novel":[53],"method":[54,61,128],"for":[55,141,179],"Encoding":[56],"Dynamic":[58],"Graph.":[59],"This":[60],"integrates":[62],"compressed":[63,175],"structural":[65],"observation":[66],"representation":[67],"cognitive":[70],"capabilities":[71],"large":[73,112],"language":[74],"purpose":[77],"integration":[80],"is":[81,183],"to":[82,133,169],"enable":[83],"advanced":[84],"question":[85],"answering":[86],"based":[87],"on":[88,97],"sequence":[90],"textual":[92,144],"scene":[93,145],"graphs.":[94],"Extended":[95],"evaluations":[96],"STAR":[99],"AGQA":[101],"datasets":[102],"indicate":[103],"that":[104,164],"outperforms":[106],"existing":[107],"by":[110,149],"margin":[113],"15-25%":[115],"addressing":[117],"queries":[118],"regarding":[119],"history":[121],"human-to-object":[123],"interactions.":[124],"Furthermore,":[125],"proposed":[127],"can":[129],"be":[130],"seamlessly":[131],"extended":[132],"process":[134],"raw":[135],"input":[136],"images":[137],"utilizing":[138],"foundational":[139],"models":[140],"extracting":[142],"explicit":[143],"graphs,":[146],"as":[147],"substantiated":[148],"results":[151],"robotic":[154,177],"experiment":[155],"conducted":[156],"wheeled":[159],"manipulator":[160],"platform.":[161],"We":[162],"hope":[163],"findings":[166],"will":[167],"contribute":[168],"implementation":[171],"robust":[173],"graph-based":[176],"memory":[178],"long-horizon":[180],"reasoning.":[181],"Code":[182],"available":[184],"at":[185],"github.com/linukc/DyGEnc.":[186]},"counts_by_year":[],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2025-10-16T00:00:00"}
