{"id":"https://openalex.org/W7164537435","doi":"https://doi.org/10.48550/arxiv.2606.12601","title":"Dual-State Slot Attention: Decoupling Appearance and Identity for Video Object-Centric Learning","display_name":"Dual-State Slot Attention: Decoupling Appearance and Identity for Video Object-Centric Learning","publication_year":2026,"publication_date":"2026-06-10","ids":{"openalex":"https://openalex.org/W7164537435","doi":"https://doi.org/10.48550/arxiv.2606.12601"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.12601","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.12601","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.2606.12601","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5116696777","display_name":"Sieu Tran","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tran, Sieu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138549291","display_name":"Duc Nguyen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nguyen, Duc","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103160077","display_name":"Hao Vo","orcid":"https://orcid.org/0009-0001-3908-4606"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Vo, Hao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138548580","display_name":"Khoa Vo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Vo, Khoa","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5138519488","display_name":"Ngan Le","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Le, Ngan","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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.24629999697208405,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.24629999697208405,"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/T11448","display_name":"Face recognition and analysis","score":0.19249999523162842,"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":0.15000000596046448,"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/spurious-relationship","display_name":"Spurious relationship","score":0.5389999747276306},{"id":"https://openalex.org/keywords/identity","display_name":"Identity (music)","score":0.534600019454956},{"id":"https://openalex.org/keywords/security-token","display_name":"Security token","score":0.5116999745368958},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.4672999978065491},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.460999995470047},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.40049999952316284},{"id":"https://openalex.org/keywords/decoupling","display_name":"Decoupling (probability)","score":0.3919000029563904},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.37940001487731934}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6690000295639038},{"id":"https://openalex.org/C97256817","wikidata":"https://www.wikidata.org/wiki/Q1462316","display_name":"Spurious relationship","level":2,"score":0.5389999747276306},{"id":"https://openalex.org/C2778355321","wikidata":"https://www.wikidata.org/wiki/Q17079427","display_name":"Identity (music)","level":2,"score":0.534600019454956},{"id":"https://openalex.org/C48145219","wikidata":"https://www.wikidata.org/wiki/Q1335365","display_name":"Security token","level":2,"score":0.5116999745368958},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5112000107765198},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5098000168800354},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.4672999978065491},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.460999995470047},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.40049999952316284},{"id":"https://openalex.org/C205606062","wikidata":"https://www.wikidata.org/wiki/Q5249645","display_name":"Decoupling (probability)","level":2,"score":0.3919000029563904},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.37940001487731934},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3050999939441681},{"id":"https://openalex.org/C48103436","wikidata":"https://www.wikidata.org/wiki/Q599031","display_name":"State (computer science)","level":2,"score":0.2994000017642975},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.2930000126361847},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.287200003862381},{"id":"https://openalex.org/C64876066","wikidata":"https://www.wikidata.org/wiki/Q5141226","display_name":"Cognitive neuroscience of visual object recognition","level":3,"score":0.2863999903202057},{"id":"https://openalex.org/C122770356","wikidata":"https://www.wikidata.org/wiki/Q1656753","display_name":"Identifiability","level":2,"score":0.2838999927043915},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.2824999988079071},{"id":"https://openalex.org/C2776459999","wikidata":"https://www.wikidata.org/wiki/Q2119376","display_name":"Fidelity","level":2,"score":0.2777999937534332},{"id":"https://openalex.org/C108154423","wikidata":"https://www.wikidata.org/wiki/Q1469792","display_name":"Salience (neuroscience)","level":2,"score":0.2590000033378601},{"id":"https://openalex.org/C124304363","wikidata":"https://www.wikidata.org/wiki/Q673661","display_name":"Abstraction","level":2,"score":0.25189998745918274},{"id":"https://openalex.org/C2780310539","wikidata":"https://www.wikidata.org/wiki/Q12547192","display_name":"Imperfect","level":2,"score":0.25189998745918274},{"id":"https://openalex.org/C37279795","wikidata":"https://www.wikidata.org/wiki/Q2492305","display_name":"Consistency model","level":3,"score":0.25099998712539673}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.12601","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.12601","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.2606.12601","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.12601","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":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Unsupervised":[0],"video":[1,227],"object-centric":[2],"learning":[3],"aims":[4],"to":[5,20,62,68,77,94],"decompose":[6],"dynamic":[7],"scenes":[8],"into":[9,136],"persistent,":[10],"object-level":[11],"representations":[12],"without":[13],"supervision.":[14],"However,":[15],"existing":[16],"slot-based":[17],"methods":[18],"struggle":[19],"maintain":[21],"stable":[22,149],"object":[23,45,150,224],"identity":[24,48,122,145,162],"in":[25,51,84],"challenging":[26],"settings":[27],"such":[28],"as":[29,173],"rapid":[30],"motion":[31],"and":[32,46,100,123,143,155,191,204,213,226,231],"partial":[33],"occlusion.":[34],"First,":[35],"they":[36],"typically":[37],"encode":[38],"both":[39],"the":[40,80,178],"per-frame":[41,141],"appearance":[42,120,142],"of":[43],"an":[44,57,144],"its":[47],"across":[49],"frames":[50],"a":[52,110,137,167,174],"single":[53],"slot":[54,63,135],"vector,":[55],"creating":[56],"objective":[58],"conflict":[59],"that":[60,114,171,207],"leads":[61],"swapping:":[64],"reconstruction":[65,154],"requires":[66,75],"sensitivity":[67],"transient":[69],"visual":[70],"changes,":[71],"whereas":[72],"temporal":[73,156,175,214],"consistency":[74,157,215],"invariance":[76],"them.":[78],"Second,":[79],"token":[81],"renormalization":[82],"used":[83],"Slot":[85,107],"Attention":[86,108],"can":[87],"amplify":[88],"weakly":[89,129,188],"attending":[90],"slots,":[91],"allowing":[92],"them":[93,193],"absorb":[95],"tokens":[96,196],"from":[97,121,128,187,194,197],"other":[98,198],"objects":[99],"destabilize":[101],"slot-to-object":[102],"correspondence.":[103],"We":[104],"propose":[105],"Dual-State":[106],"(DSSA),":[109],"fully":[111],"self-supervised":[112],"framework":[113],"addresses":[115],"these":[116],"limitations":[117],"by":[118,124],"separating":[119],"reducing":[125],"spurious":[126],"updates":[127,186],"matching":[130,189],"slots.":[131],"DSSA":[132,208],"decomposes":[133],"each":[134],"local":[138,179],"state":[139,146,163],"for":[140,147],"temporally":[148],"information,":[151],"thereby":[152],"aligning":[153],"with":[158],"separate":[159],"representations.":[160],"The":[161],"is":[164],"updated":[165],"through":[166],"learned":[168],"recurrent":[169],"transition":[170],"acts":[172],"filter":[176],"on":[177,201],"state,":[180],"while":[181,219],"competition-modulated":[182],"aggregation":[183],"(CMA)":[184],"down-weights":[185],"slots":[190],"prevents":[192],"absorbing":[195],"objects.":[199],"Experiments":[200],"MOVi-C,":[202],"MOVi-D,":[203],"YouTube-VIS":[205],"demonstrate":[206],"consistently":[209],"improves":[210],"segmentation":[211],"quality":[212],"over":[216],"prior":[217],"methods,":[218],"also":[220],"yielding":[221],"stronger":[222],"downstream":[223],"recognition":[225],"dynamics":[228],"prediction.":[229],"Code":[230],"models":[232],"will":[233],"be":[234],"made":[235],"publicly":[236],"available":[237],"upon":[238],"acceptance.":[239]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-13T00:00:00"}