{"id":"https://openalex.org/W7162410392","doi":"https://doi.org/10.48550/arxiv.2605.24037","title":"Mode-as-Sequence: Translating Multimodal Motion Prediction into Unified Sequential Mode Modeling","display_name":"Mode-as-Sequence: Translating Multimodal Motion Prediction into Unified Sequential Mode Modeling","publication_year":2026,"publication_date":"2026-05-21","ids":{"openalex":"https://openalex.org/W7162410392","doi":"https://doi.org/10.48550/arxiv.2605.24037"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.24037","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.24037","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":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.2605.24037","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5137007928","display_name":"Zikang Zhou","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhou, Zikang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137068208","display_name":"Haibo Hu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hu, Haibo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5071933192","display_name":"Xinhong Chen","orcid":"https://orcid.org/0000-0002-8563-148X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Xinhong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137073169","display_name":"Yifan Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Yifan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137084271","display_name":"Nan Guan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Guan, Nan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137000710","display_name":"Yung-Hui Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Yung-Hui","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137072506","display_name":"Chun Jason Xue","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xue, Chun Jason","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5137067704","display_name":"Jianping Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Jianping","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/T10812","display_name":"Human Pose and Action Recognition","score":0.14749999344348907,"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/T10812","display_name":"Human Pose and Action Recognition","score":0.14749999344348907,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.1340000033378601,"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/T12290","display_name":"Human Motion and Animation","score":0.10159999877214432,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/ranking","display_name":"Ranking (information retrieval)","score":0.6100999712944031},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5911999940872192},{"id":"https://openalex.org/keywords/mode","display_name":"Mode (computer interface)","score":0.5863000154495239},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5315999984741211},{"id":"https://openalex.org/keywords/sequence","display_name":"Sequence (biology)","score":0.5030999779701233},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.4941999912261963},{"id":"https://openalex.org/keywords/decoding-methods","display_name":"Decoding methods","score":0.4661000072956085},{"id":"https://openalex.org/keywords/motion","display_name":"Motion (physics)","score":0.45809999108314514}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7452999949455261},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.6100999712944031},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5911999940872192},{"id":"https://openalex.org/C48677424","wikidata":"https://www.wikidata.org/wiki/Q6888088","display_name":"Mode (computer interface)","level":2,"score":0.5863000154495239},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5631999969482422},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5315999984741211},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.5030999779701233},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.4941999912261963},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.48420000076293945},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.4661000072956085},{"id":"https://openalex.org/C104114177","wikidata":"https://www.wikidata.org/wiki/Q79782","display_name":"Motion (physics)","level":2,"score":0.45809999108314514},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.4316999912261963},{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.42809998989105225},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.390500009059906},{"id":"https://openalex.org/C2777472644","wikidata":"https://www.wikidata.org/wiki/Q16968992","display_name":"Approximate inference","level":3,"score":0.37290000915527344},{"id":"https://openalex.org/C19768560","wikidata":"https://www.wikidata.org/wiki/Q320727","display_name":"Dependency (UML)","level":2,"score":0.3522000014781952},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.349700003862381},{"id":"https://openalex.org/C2778029271","wikidata":"https://www.wikidata.org/wiki/Q5421931","display_name":"Extension (predicate logic)","level":2,"score":0.34209999442100525},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.33869999647140503},{"id":"https://openalex.org/C2778067643","wikidata":"https://www.wikidata.org/wiki/Q166507","display_name":"Interval (graph theory)","level":2,"score":0.3352999985218048},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.32739999890327454},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.30570000410079956},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.29589998722076416},{"id":"https://openalex.org/C2781020372","wikidata":"https://www.wikidata.org/wiki/Q533093","display_name":"On the fly","level":2,"score":0.2833000123500824}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.24037","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.24037","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":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.24037","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.24037","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":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":{"Multimodal":[0],"motion":[1,201],"forecasting":[2],"is":[3,83],"inherently":[4],"under-supervised:":[5],"each":[6,81],"training":[7],"scene":[8],"provides":[9],"only":[10],"one":[11],"realized":[12],"future,":[13],"yet":[14],"multiple":[15],"plausible":[16],"futures":[17],"exist.":[18],"This":[19],"sparse":[20,145],"supervision":[21],"often":[22],"leads":[23],"to":[24],"mode":[25,31,55,60,78,82],"collapse":[26],"(redundant":[27],"hypotheses":[28,94],"and":[29,33,62,133,141,151,178,184,204,224],"insufficient":[30],"coverage)":[32],"unreliable":[34],"confidence":[35,97,143,164],"ranking":[36,160],"when":[37],"predicting":[38],"a":[39,47,125,158],"small":[40],"set":[41,56],"of":[42,219],"trajectories.":[43],"We":[44],"propose":[45,107],"Mode-as-Sequence,":[46],"unified":[48],"decoding":[49,121],"framework":[50],"that":[51,162],"translates":[52],"an":[53,58],"unordered":[54],"into":[57],"ordered":[59],"sequence":[61],"explicitly":[63],"models":[64],"mode-to-mode":[65,118],"dependency.":[66],"Under":[67],"this":[68],"framework,":[69],"we":[70,105,147],"develop":[71],"two":[72],"complementary":[73],"instantiations.":[74],"ModeSeq":[75,193,206],"performs":[76],"recurrent":[77],"decoding,":[79],"where":[80],"generated":[84,89],"conditioned":[85],"on":[86,168],"the":[87,101,112,188,198,211,217],"previously":[88],"modes,":[90],"encouraging":[91],"diverse,":[92],"non-redundant":[93],"with":[95,157],"calibrated":[96,142],"ordering.":[98],"To":[99,137],"remove":[100],"mode-by-mode":[102],"autoregressive":[103],"bottleneck,":[104],"further":[106],"Parallel":[108,205],"ModeSeq,":[109],"which":[110],"preserves":[111],"same":[113],"causal":[114],"dependency":[115],"using":[116],"masked":[117],"self-attention":[119],"while":[120],"all":[122],"modes":[123,140],"in":[124,174,197,210],"single":[126],"forward":[127],"pass,":[128],"enabling":[129],"efficient":[130],"large-$K$":[131],"inference":[132],"scalable":[134],"joint-scene":[135,153],"prediction.":[136],"learn":[138],"representative":[139],"under":[144],"labels,":[146],"introduce":[148],"Early-Match-Take-All":[149],"(EMTA)":[150],"its":[152],"extension":[154],"MA-EMTA,":[155],"together":[156],"lightweight":[159],"regularizer":[161],"reduces":[163],"inversions.":[165],"Extensive":[166],"experiments":[167],"large-scale":[169],"benchmarks":[170],"demonstrate":[171],"consistent":[172],"improvements":[173],"both":[175,222],"ranking-oriented":[176],"metrics":[177],"best-of-K":[179],"accuracy":[180,223],"across":[181],"datasets,":[182],"horizons,":[183],"object":[185],"types.":[186],"In":[187],"Waymo":[189],"Open":[190],"Dataset":[191],"challenges,":[192],"achieves":[194,207],"1st":[195,208],"place":[196,209],"2024":[199],"LiDAR-free":[200],"prediction":[202],"track,":[203],"2025":[212],"Interaction":[213],"Prediction":[214],"Challenge,":[215],"validating":[216],"effectiveness":[218],"Mode-as-Sequence":[220],"for":[221],"efficiency.":[225]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-27T00:00:00"}
