{"id":"https://openalex.org/W7163033160","doi":"https://doi.org/10.48550/arxiv.2605.31115","title":"Polyphony: Diffusion-based Dual-Hand Action Segmentation with Alternating Vision Transformer and Semantic Conditioning","display_name":"Polyphony: Diffusion-based Dual-Hand Action Segmentation with Alternating Vision Transformer and Semantic Conditioning","publication_year":2026,"publication_date":"2026-05-29","ids":{"openalex":"https://openalex.org/W7163033160","doi":"https://doi.org/10.48550/arxiv.2605.31115"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.31115","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.31115","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.2605.31115","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5137550082","display_name":"Hao Zheng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zheng, Hao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137533038","display_name":"Hu Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Hu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137579358","display_name":"Tiantian Zheng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zheng, Tiantian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103222950","display_name":"Prajjwal Bhattarai","orcid":"https://orcid.org/0000-0002-0702-414X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bhattarai, Prajjwal","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5134210746","display_name":"Tuka Alhanai","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Alhanai, Tuka","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":0.9779999852180481,"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":0.9779999852180481,"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/T10510","display_name":"Stroke Rehabilitation and Recovery","score":0.003599999938160181,"subfield":{"id":"https://openalex.org/subfields/2742","display_name":"Rehabilitation"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T11431","display_name":"Action Observation and Synchronization","score":0.00279999990016222,"subfield":{"id":"https://openalex.org/subfields/3207","display_name":"Social Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/ambiguity","display_name":"Ambiguity","score":0.6593999862670898},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6108999848365784},{"id":"https://openalex.org/keywords/weighting","display_name":"Weighting","score":0.5390999913215637},{"id":"https://openalex.org/keywords/pairwise-comparison","display_name":"Pairwise comparison","score":0.5144000053405762},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.48399999737739563},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.47279998660087585},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.37860000133514404},{"id":"https://openalex.org/keywords/action","display_name":"Action (physics)","score":0.3440000116825104}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7555999755859375},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6861000061035156},{"id":"https://openalex.org/C2780522230","wikidata":"https://www.wikidata.org/wiki/Q1140419","display_name":"Ambiguity","level":2,"score":0.6593999862670898},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6108999848365784},{"id":"https://openalex.org/C183115368","wikidata":"https://www.wikidata.org/wiki/Q856577","display_name":"Weighting","level":2,"score":0.5390999913215637},{"id":"https://openalex.org/C184898388","wikidata":"https://www.wikidata.org/wiki/Q1435712","display_name":"Pairwise comparison","level":2,"score":0.5144000053405762},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.48399999737739563},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.47279998660087585},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.39809998869895935},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.37860000133514404},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.36469998955726624},{"id":"https://openalex.org/C2780791683","wikidata":"https://www.wikidata.org/wiki/Q846785","display_name":"Action (physics)","level":2,"score":0.3440000116825104},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.3125999867916107},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.30169999599456787},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.30059999227523804},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3001999855041504},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.29910001158714294},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.29580000042915344},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.2948000133037567},{"id":"https://openalex.org/C2983787585","wikidata":"https://www.wikidata.org/wiki/Q93586","display_name":"Feature matching","level":3,"score":0.29030001163482666},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.26759999990463257},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.25589999556541443}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.31115","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.31115","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.2605.31115","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.31115","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":[{"score":0.5030605792999268,"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10"},{"score":0.48914387822151184,"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Dual-hand":[0],"action":[1,95],"segmentation,":[2],"densely":[3],"predicting":[4],"actions":[5],"for":[6,14,112,119],"both":[7,77,126],"hands":[8,78],"from":[9,76],"untrimmed":[10],"videos,":[11],"is":[12,171],"essential":[13],"understanding":[15],"complex":[16,25],"bimanual":[17],"activities.":[18],"However,":[19],"it":[20],"poses":[21],"several":[22],"unique":[23],"challenges:":[24],"inter-hand":[26,113],"dependencies,":[27],"visual":[28,90],"asymmetry":[29],"between":[30,66],"hands,":[31],"representation":[32],"conflicts":[33],"where":[34],"the":[35,139,145],"dominant":[36],"hand":[37],"monopolizes":[38],"gradients,":[39],"and":[40,68,104,115,137],"semantic":[41],"ambiguity":[42],"in":[43],"fine-grained":[44],"actions.":[45],"We":[46],"propose":[47],"Polyphony,":[48],"a":[49,81,151,160],"three-stage":[50],"method":[51,148],"to":[52,71,97,134],"address":[53],"these":[54],"challenges":[55],"through:":[56],"(1)":[57],"an":[58],"Alternating":[59],"Dual-Hand":[60],"Vision":[61],"Transformer":[62],"that":[63,88,149],"alternates":[64],"training":[65],"left-":[67],"right-hand":[69],"mini-batches":[70],"ensure":[72],"balanced":[73],"gradient":[74],"contributions":[75],"while":[79],"sharing":[80],"spatio-temporal":[82],"encoder;":[83],"(2)":[84],"Semantic":[85],"Feature":[86],"Conditioning":[87],"aligns":[89],"features":[91],"with":[92,108,131,159],"structured,":[93],"compositional":[94],"descriptions":[96],"enhance":[98],"discrimination":[99],"of":[100],"semantically":[101],"similar":[102],"actions;":[103],"(3)":[105],"Diffusion-Based":[106],"Segmentation":[107],"cross-hand":[109],"feature":[110],"fusion":[111],"coordination":[114],"adaptive":[116],"loss":[117],"weighting":[118],"balancing":[120],"performance.":[121],"Polyphony":[122],"achieves":[123],"state-of-the-art":[124],"on":[125,138],"dual-hand":[127],"datasets":[128],"(HA-ViD,":[129],"ATTACH)":[130],"improvements":[132],"up":[133],"16.8":[135],"points,":[136],"single-stream":[140],"Breakfast":[141],"dataset":[142],"(82.5%),":[143],"outperforming":[144],"prior":[146],"best":[147],"uses":[150],"12x":[152],"larger":[153],"backbone.":[154],"Notably,":[155],"our":[156],"unified":[157],"model":[158],"single":[161],"shared":[162],"backbone":[163],"surpasses":[164],"baselines":[165],"requiring":[166],"separate":[167],"per-hand":[168],"models.":[169],"Code":[170],"at":[172],"https://github.com/x-labs-xyz/Polyphony-Dual-hand-Action-Segmentation.":[173]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-02T00:00:00"}