{"id":"https://openalex.org/W7154281479","doi":"https://doi.org/10.48550/arxiv.2604.10078","title":"Attention-Guided Dual-Stream Learning for Group Engagement Recognition: Fusing Transformer-Encoded Motion Dynamics with Scene Context via Adaptive Gating","display_name":"Attention-Guided Dual-Stream Learning for Group Engagement Recognition: Fusing Transformer-Encoded Motion Dynamics with Scene Context via Adaptive Gating","publication_year":2026,"publication_date":"2026-04-11","ids":{"openalex":"https://openalex.org/W7154281479","doi":"https://doi.org/10.48550/arxiv.2604.10078"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.10078","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.10078","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":"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.2604.10078","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5133587078","display_name":"Saniah Kayenat Chowdhury","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chowdhury, Saniah Kayenat","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5133616395","display_name":"Muhammad E. H. Chowdhury","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chowdhury, Muhammad E. H.","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":0.7893000245094299,"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":0.7893000245094299,"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":0.05380000174045563,"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"}},{"id":"https://openalex.org/T12290","display_name":"Human Motion and Animation","score":0.021900000050663948,"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/optical-flow","display_name":"Optical flow","score":0.5672000050544739},{"id":"https://openalex.org/keywords/pooling","display_name":"Pooling","score":0.5440999865531921},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.4700999855995178},{"id":"https://openalex.org/keywords/motion","display_name":"Motion (physics)","score":0.4575999975204468},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.38839998841285706},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.37709999084472656},{"id":"https://openalex.org/keywords/joint","display_name":"Joint (building)","score":0.3700999915599823},{"id":"https://openalex.org/keywords/encoding","display_name":"Encoding (memory)","score":0.3562999963760376},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.34209999442100525},{"id":"https://openalex.org/keywords/context-model","display_name":"Context model","score":0.32749998569488525}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7175999879837036},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5741000175476074},{"id":"https://openalex.org/C155542232","wikidata":"https://www.wikidata.org/wiki/Q736111","display_name":"Optical flow","level":3,"score":0.5672000050544739},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.5440999865531921},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.4700999855995178},{"id":"https://openalex.org/C104114177","wikidata":"https://www.wikidata.org/wiki/Q79782","display_name":"Motion (physics)","level":2,"score":0.4575999975204468},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.40290001034736633},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.38839998841285706},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.37709999084472656},{"id":"https://openalex.org/C18555067","wikidata":"https://www.wikidata.org/wiki/Q8375051","display_name":"Joint (building)","level":2,"score":0.3700999915599823},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.3562999963760376},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.34209999442100525},{"id":"https://openalex.org/C183322885","wikidata":"https://www.wikidata.org/wiki/Q17007702","display_name":"Context model","level":3,"score":0.32749998569488525},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.32269999384880066},{"id":"https://openalex.org/C121687571","wikidata":"https://www.wikidata.org/wiki/Q4677630","display_name":"Activity recognition","level":2,"score":0.32170000672340393},{"id":"https://openalex.org/C152565575","wikidata":"https://www.wikidata.org/wiki/Q1124538","display_name":"Conditional random field","level":2,"score":0.31839999556541443},{"id":"https://openalex.org/C138020889","wikidata":"https://www.wikidata.org/wiki/Q2349659","display_name":"Collaborative learning","level":2,"score":0.31439998745918274},{"id":"https://openalex.org/C145912823","wikidata":"https://www.wikidata.org/wiki/Q113558","display_name":"Dynamics (music)","level":2,"score":0.31380000710487366},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2953000068664551},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.29260000586509705},{"id":"https://openalex.org/C194519906","wikidata":"https://www.wikidata.org/wiki/Q7627827","display_name":"Student engagement","level":2,"score":0.2897999882698059},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.28839999437332153},{"id":"https://openalex.org/C2781311116","wikidata":"https://www.wikidata.org/wiki/Q83306","display_name":"Group (periodic table)","level":2,"score":0.28119999170303345},{"id":"https://openalex.org/C2775936607","wikidata":"https://www.wikidata.org/wiki/Q466845","display_name":"Tracking (education)","level":2,"score":0.2757999897003174},{"id":"https://openalex.org/C194544171","wikidata":"https://www.wikidata.org/wiki/Q21105679","display_name":"Gating","level":2,"score":0.27320000529289246},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.26739999651908875},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.26669999957084656},{"id":"https://openalex.org/C66746571","wikidata":"https://www.wikidata.org/wiki/Q1134833","display_name":"ENCODE","level":3,"score":0.2623000144958496},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.26100000739097595},{"id":"https://openalex.org/C2987834672","wikidata":"https://www.wikidata.org/wiki/Q4677630","display_name":"Action recognition","level":3,"score":0.2556999921798706},{"id":"https://openalex.org/C2776020993","wikidata":"https://www.wikidata.org/wiki/Q2305340","display_name":"Group work","level":2,"score":0.25029999017715454},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.2500999867916107}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.10078","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.10078","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":"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.2604.10078","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.10078","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":"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":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Student":[0],"engagement":[1,28,38,55,62,229],"is":[2,223],"crucial":[3],"for":[4,33,53],"improving":[5],"learning":[6],"outcomes":[7],"in":[8,227],"group":[9,22,166],"activities.":[10],"Highly":[11],"engaged":[12],"students":[13],"perform":[14],"better":[15],"both":[16,68,154],"individually":[17],"and":[18,70,82,104],"contribute":[19],"to":[20,231],"overall":[21],"success.":[23],"However,":[24],"most":[25],"existing":[26],"automated":[27],"recognition":[29,56,230],"methods":[30],"are":[31,137],"designed":[32],"online":[34],"classrooms":[35],"or":[36],"estimate":[37],"at":[39],"the":[40,90,124,150,170,177,203,218,225],"individual":[41,69,162],"level.":[42],"Addressing":[43],"this":[44],"gap,":[45],"we":[46,208],"propose":[47],"DualEngage,":[48],"a":[49,64,101,113,129,158,196,233],"novel":[50],"two-stream":[51,135,219],"framework":[52],"group-level":[54,71],"from":[57,123],"in-classroom":[58],"videos.":[59],"It":[60],"models":[61,76],"as":[63,241],"joint":[65,151,159],"function":[66],"of":[67,153,161,186,193,199,205],"behaviors.":[72],"The":[73,116,134],"primary":[74],"stream":[75,118],"person-level":[77],"motion":[78,98,239],"dynamics":[79],"by":[80,183],"detecting":[81],"tracking":[83],"students,":[84],"extracting":[85],"dense":[86],"optical":[87],"flow":[88],"with":[89,164,195],"Recurrent":[91],"All-Pairs":[92],"Field":[93],"Transforms":[94],"network,":[95],"encoding":[96],"temporal":[97],"patterns":[99],"using":[100,173],"transformer":[102],"encoder,":[103],"finally":[105],"aggregating":[106],"per-student":[107],"representations":[108,136],"through":[109],"attention":[110],"pooling":[111],"into":[112],"unified":[114],"representation.":[115],"secondary":[117],"captures":[119],"scene-level":[120],"spatiotemporal":[121],"information":[122],"full":[125],"video":[126],"clip,":[127],"leveraging":[128],"pretrained":[130],"three-dimensional":[131],"Residual":[132],"Network.":[133],"combined":[138],"via":[139],"softmax-gated":[140],"fusion,":[141],"which":[142],"dynamically":[143],"weights":[144],"each":[145,206],"stream's":[146],"contribution":[147,204],"based":[148],"on":[149,176],"context":[152],"features.":[155],"DualEngage":[156],"learns":[157],"representation":[160],"actions":[163],"overarching":[165],"dynamics.":[167],"We":[168],"evaluate":[169],"proposed":[171],"approach":[172],"fivefold":[174],"cross-validation":[175],"Classroom":[178],"Group":[179],"Engagement":[180],"Dataset":[181],"developed":[182],"Ocean":[184],"University":[185],"China,":[187],"achieving":[188],"an":[189,211,242],"average":[190],"classification":[191],"accuracy":[192],"0.9621+/-0.0161":[194],"macro-averaged":[197],"F1":[198],"0.9530+/-0.0204.":[200],"To":[201],"understand":[202],"branch,":[207],"further":[209],"conduct":[210],"ablation":[212],"study":[213],"comparing":[214],"single-stream":[215],"variants":[216],"against":[217],"model.":[220],"This":[221],"work":[222],"among":[224],"first":[226],"classroom":[228],"adopt":[232],"dual-stream":[234],"design":[235],"that":[236],"explicitly":[237],"leverages":[238],"cues":[240],"estimator.":[243]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-04-15T00:00:00"}
