{"id":"https://openalex.org/W7140494291","doi":"https://doi.org/10.48550/arxiv.2603.23757","title":"Learning Cross-Joint Attention for Generalizable Video-Based Seizure Detection","display_name":"Learning Cross-Joint Attention for Generalizable Video-Based Seizure Detection","publication_year":2026,"publication_date":"2026-03-24","ids":{"openalex":"https://openalex.org/W7140494291","doi":"https://doi.org/10.48550/arxiv.2603.23757"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.23757","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.23757","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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.2603.23757","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5130676025","display_name":"Omar Zamzam","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zamzam, Omar","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5012194214","display_name":"Takfarinas Medani","orcid":"https://orcid.org/0000-0003-0774-067X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Medani, Takfarinas","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5087331400","display_name":"Chinmay Chinara","orcid":"https://orcid.org/0000-0002-4474-1359"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chinara, Chinmay","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5130681295","display_name":"Richard Leahy","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Leahy, Richard","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/T10429","display_name":"EEG and Brain-Computer Interfaces","score":0.9624999761581421,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},"topics":[{"id":"https://openalex.org/T10429","display_name":"EEG and Brain-Computer Interfaces","score":0.9624999761581421,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T10812","display_name":"Human Pose and Action Recognition","score":0.006500000134110451,"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/T10667","display_name":"Emotion and Mood Recognition","score":0.003599999938160181,"subfield":{"id":"https://openalex.org/subfields/3205","display_name":"Experimental and Cognitive 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/clips","display_name":"CLIPS","score":0.7063999772071838},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.35760000348091125},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.2913999855518341},{"id":"https://openalex.org/keywords/dynamics","display_name":"Dynamics (music)","score":0.2840000092983246},{"id":"https://openalex.org/keywords/video-recording","display_name":"Video recording","score":0.2653000056743622}],"concepts":[{"id":"https://openalex.org/C2778739407","wikidata":"https://www.wikidata.org/wiki/Q165372","display_name":"CLIPS","level":2,"score":0.7063999772071838},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6259999871253967},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.600600004196167},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4113999903202057},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.35760000348091125},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3151000142097473},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.30559998750686646},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2913999855518341},{"id":"https://openalex.org/C145912823","wikidata":"https://www.wikidata.org/wiki/Q113558","display_name":"Dynamics (music)","level":2,"score":0.2840000092983246},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.2743000090122223},{"id":"https://openalex.org/C2988634675","wikidata":"https://www.wikidata.org/wiki/Q34508","display_name":"Video recording","level":2,"score":0.2653000056743622},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.26409998536109924},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2508000135421753}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.23757","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.23757","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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.2603.23757","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.23757","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":{"Automated":[0],"seizure":[1,101],"detection":[2],"from":[3],"long-term":[4],"clinical":[5],"videos":[6],"can":[7],"substantially":[8],"reduce":[9],"manual":[10],"review":[11],"time":[12],"and":[13,32,62,81,89,116],"enable":[14],"real-time":[15],"monitoring.":[16],"However,":[17],"existing":[18],"video-based":[19],"methods":[20],"often":[21],"struggle":[22],"to":[23,25,29,50,86],"generalize":[24],"unseen":[26,120],"subjects":[27],"due":[28],"background":[30,68],"bias":[31],"reliance":[33],"on":[34,47,119],"subject-specific":[35],"appearance":[36],"cues.":[37],"We":[38],"propose":[39],"a":[40,76],"joint-centric":[41],"attention":[42,83],"model":[43,87],"that":[44,107],"focuses":[45],"exclusively":[46],"body":[48,58,93],"dynamics":[49],"improve":[51],"cross-subject":[52,104],"generalization.":[53],"For":[54],"each":[55],"video":[56],"segment,":[57],"joints":[59],"are":[60,65,73],"detected":[61],"joint-centered":[63,71],"clips":[64,72],"extracted,":[66],"suppressing":[67],"context.":[69],"These":[70],"tokenized":[74],"using":[75],"Video":[77],"Vision":[78],"Transformer":[79],"(ViViT),":[80],"cross-joint":[82],"is":[84],"learned":[85],"spatial":[88],"temporal":[90],"interactions":[91],"between":[92],"parts,":[94],"capturing":[95],"coordinated":[96],"movement":[97],"patterns":[98],"characteristic":[99],"of":[100],"semiology.":[102],"Extensive":[103],"experiments":[105],"show":[106],"the":[108],"proposed":[109],"method":[110],"consistently":[111],"outperforms":[112],"state-of-the-art":[113],"CNN-,":[114],"graph-,":[115],"transformer-based":[117],"approaches":[118],"subjects.":[121]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-03-27T00:00:00"}