{"id":"https://openalex.org/W7147287824","doi":"https://doi.org/10.48550/arxiv.2603.29692","title":"SkeletonContext: Skeleton-side Context Prompt Learning for Zero-Shot Skeleton-based Action Recognition","display_name":"SkeletonContext: Skeleton-side Context Prompt Learning for Zero-Shot Skeleton-based Action Recognition","publication_year":2026,"publication_date":"2026-03-31","ids":{"openalex":"https://openalex.org/W7147287824","doi":"https://doi.org/10.48550/arxiv.2603.29692"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.29692","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.29692","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.2603.29692","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5132599398","display_name":"Ning Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Ning","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132677345","display_name":"Tieyue Wu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wu, Tieyue","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044043482","display_name":"Naeha Sharif","orcid":"https://orcid.org/0000-0001-8351-6288"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sharif, Naeha","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132591488","display_name":"Farid Boussaid","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Boussaid, Farid","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5028794275","display_name":"Guangming Zhu","orcid":"https://orcid.org/0000-0003-3214-4095"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhu, Guangming","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132680043","display_name":"Lin Mei","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mei, Lin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5127837827","display_name":"Mohammed Bennamoun","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bennamoun, Mohammed","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5132634820","display_name":"zhang liang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"liang, zhang","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":0.9398999810218811,"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":0.9398999810218811,"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":0.025800000876188278,"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/T10653","display_name":"Robot Manipulation and Learning","score":0.005400000140070915,"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/action","display_name":"Action (physics)","score":0.5842999815940857},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5644999742507935},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.5187000036239624},{"id":"https://openalex.org/keywords/action-recognition","display_name":"Action recognition","score":0.39959999918937683},{"id":"https://openalex.org/keywords/context-model","display_name":"Context model","score":0.3813999891281128},{"id":"https://openalex.org/keywords/motion","display_name":"Motion (physics)","score":0.3725999891757965},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.3714999854564667},{"id":"https://openalex.org/keywords/cognitive-neuroscience-of-visual-object-recognition","display_name":"Cognitive neuroscience of visual object recognition","score":0.3677999973297119},{"id":"https://openalex.org/keywords/joint","display_name":"Joint (building)","score":0.3587000072002411}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7732999920845032},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6151000261306763},{"id":"https://openalex.org/C2780791683","wikidata":"https://www.wikidata.org/wiki/Q846785","display_name":"Action (physics)","level":2,"score":0.5842999815940857},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5644999742507935},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.5406000018119812},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.5187000036239624},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.4099999964237213},{"id":"https://openalex.org/C2987834672","wikidata":"https://www.wikidata.org/wiki/Q4677630","display_name":"Action recognition","level":3,"score":0.39959999918937683},{"id":"https://openalex.org/C183322885","wikidata":"https://www.wikidata.org/wiki/Q17007702","display_name":"Context model","level":3,"score":0.3813999891281128},{"id":"https://openalex.org/C104114177","wikidata":"https://www.wikidata.org/wiki/Q79782","display_name":"Motion (physics)","level":2,"score":0.3725999891757965},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.3714999854564667},{"id":"https://openalex.org/C64876066","wikidata":"https://www.wikidata.org/wiki/Q5141226","display_name":"Cognitive neuroscience of visual object recognition","level":3,"score":0.3677999973297119},{"id":"https://openalex.org/C18555067","wikidata":"https://www.wikidata.org/wiki/Q8375051","display_name":"Joint (building)","level":2,"score":0.3587000072002411},{"id":"https://openalex.org/C2777508537","wikidata":"https://www.wikidata.org/wiki/Q7936620","display_name":"Visual reasoning","level":2,"score":0.32820001244544983},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.3222000002861023},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.320499986410141},{"id":"https://openalex.org/C194995250","wikidata":"https://www.wikidata.org/wiki/Q531136","display_name":"Affordance","level":2,"score":0.3125},{"id":"https://openalex.org/C100609095","wikidata":"https://www.wikidata.org/wiki/Q1335050","display_name":"Embodied cognition","level":2,"score":0.31220000982284546},{"id":"https://openalex.org/C18969341","wikidata":"https://www.wikidata.org/wiki/Q1169129","display_name":"Skeleton (computer programming)","level":2,"score":0.3102000057697296},{"id":"https://openalex.org/C205606062","wikidata":"https://www.wikidata.org/wiki/Q5249645","display_name":"Decoupling (probability)","level":2,"score":0.3018999993801117},{"id":"https://openalex.org/C86034646","wikidata":"https://www.wikidata.org/wiki/Q474311","display_name":"Semantic gap","level":4,"score":0.29269999265670776},{"id":"https://openalex.org/C64754055","wikidata":"https://www.wikidata.org/wiki/Q7574053","display_name":"Spatial contextual awareness","level":2,"score":0.2881999909877777},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.28600001335144043},{"id":"https://openalex.org/C31170391","wikidata":"https://www.wikidata.org/wiki/Q188619","display_name":"Hierarchy","level":2,"score":0.26840001344680786},{"id":"https://openalex.org/C521332185","wikidata":"https://www.wikidata.org/wiki/Q185816","display_name":"Analogy","level":2,"score":0.25999999046325684},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2574999928474426},{"id":"https://openalex.org/C2983448237","wikidata":"https://www.wikidata.org/wiki/Q1078276","display_name":"Language understanding","level":2,"score":0.25209999084472656}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.29692","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.29692","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.2603.29692","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.29692","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":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Zero-shot":[0],"skeleton-based":[1],"action":[2,138],"recognition":[3],"aims":[4],"to":[5,58,95,111,131],"recognize":[6],"unseen":[7],"actions":[8],"by":[9],"transferring":[10],"knowledge":[11],"from":[12,103],"seen":[13],"categories":[14],"through":[15],"semantic":[16,53,117],"descriptions.":[17],"Most":[18],"existing":[19],"methods":[20],"typically":[21],"align":[22],"skeleton":[23,51,113],"features":[24],"with":[25,77],"textual":[26],"embeddings":[27],"within":[28],"a":[29,69,84,91,125],"shared":[30],"latent":[31],"space.":[32],"However,":[33],"the":[34,44,112,142],"absence":[35,143],"of":[36,144],"contextual":[37,79,98],"cues,":[38],"such":[39],"as":[40],"objects":[41],"involved":[42],"in":[43,141,169],"action,":[45],"introduces":[46],"an":[47],"inherent":[48],"gap":[49],"between":[50],"and":[52,119,162,173],"representations,":[54],"making":[55],"it":[56],"difficult":[57],"distinguish":[59],"visually":[60,176],"similar":[61,177],"actions.":[62,178],"To":[63],"address":[64],"this,":[65],"we":[66,82],"propose":[67],"SkeletonContext,":[68],"prompt-based":[70],"framework":[71],"that":[72,154],"enriches":[73],"skeletal":[74],"motion":[75],"representations":[76],"language-driven":[78],"semantics.":[80],"Specifically,":[81],"introduce":[83],"Cross-Modal":[85],"Context":[86],"Prompt":[87],"Module,":[88],"which":[89],"leverages":[90],"pretrained":[92],"language":[93],"model":[94],"reconstruct":[96],"masked":[97],"prompts":[99],"under":[100,159],"guidance":[101],"derived":[102],"LLMs.":[104],"This":[105],"design":[106],"effectively":[107],"transfers":[108],"linguistic":[109],"context":[110,172],"encoder":[114],"for":[115],"instance-level":[116],"grounding":[118],"improved":[120],"cross-modal":[121],"alignment.":[122],"In":[123],"addition,":[124],"Key-Part":[126],"Decoupling":[127],"Module":[128],"is":[129],"incorporated":[130],"decouple":[132],"motion-relevant":[133],"joint":[134],"features,":[135],"ensuring":[136],"robust":[137],"understanding":[139],"even":[140],"explicit":[145],"object":[146],"interactions.":[147],"Extensive":[148],"experiments":[149],"on":[150],"multiple":[151],"benchmarks":[152],"demonstrate":[153],"SkeletonContext":[155],"achieves":[156],"state-of-the-art":[157],"performance":[158],"both":[160],"conventional":[161],"generalized":[163],"zero-shot":[164],"settings,":[165],"validating":[166],"its":[167],"effectiveness":[168],"reasoning":[170],"about":[171],"distinguishing":[174],"fine-grained,":[175]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-04-02T00:00:00"}
