{"id":"https://openalex.org/W2980909283","doi":"https://doi.org/10.1145/3340555.3355717","title":"Multi-feature and Multi-instance Learning with Anti-overfitting Strategy for Engagement Intensity Prediction","display_name":"Multi-feature and Multi-instance Learning with Anti-overfitting Strategy for Engagement Intensity Prediction","publication_year":2019,"publication_date":"2019-10-14","ids":{"openalex":"https://openalex.org/W2980909283","doi":"https://doi.org/10.1145/3340555.3355717","mag":"2980909283"},"language":"en","primary_location":{"id":"doi:10.1145/3340555.3355717","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3340555.3355717","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 International Conference on Multimodal Interaction","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5108047851","display_name":"Jianming Wu","orcid":"https://orcid.org/0000-0002-6136-7469"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Jianming Wu","raw_affiliation_strings":["Tokyo AI Team"],"affiliations":[{"raw_affiliation_string":"Tokyo AI Team","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5072175550","display_name":"Zhiguang Zhou","orcid":"https://orcid.org/0009-0001-4783-6863"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhiguang Zhou","raw_affiliation_strings":["Tokyo AI Team"],"affiliations":[{"raw_affiliation_string":"Tokyo AI Team","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5115694972","display_name":"Yanan Wang","orcid":"https://orcid.org/0000-0001-7179-4686"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yanan Wang","raw_affiliation_strings":["Tokyo AI Team"],"affiliations":[{"raw_affiliation_string":"Tokyo AI Team","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100421669","display_name":"Yi Li","orcid":"https://orcid.org/0000-0002-9465-0869"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yi Li","raw_affiliation_strings":["Tokyo AI Team"],"affiliations":[{"raw_affiliation_string":"Tokyo AI Team","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014155585","display_name":"Xin Xu","orcid":"https://orcid.org/0000-0002-3080-4996"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xin Xu","raw_affiliation_strings":["Tokyo AI Team"],"affiliations":[{"raw_affiliation_string":"Tokyo AI Team","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5068788899","display_name":"Yusuke Uchida","orcid":"https://orcid.org/0000-0002-6932-1465"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yusuke Uchida","raw_affiliation_strings":[""],"affiliations":[{"raw_affiliation_string":"","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5108047851"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.5061,"has_fulltext":false,"cited_by_count":13,"citation_normalized_percentile":{"value":0.69662509,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"582","last_page":"588"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10812","display_name":"Human Pose and Action Recognition","score":0.9994999766349792,"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":0.9994999766349792,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9652000069618225,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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":0.949999988079071,"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/overfitting","display_name":"Overfitting","score":0.9792475700378418},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7715953588485718},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7400550842285156},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6558682918548584},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5269815921783447},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5079815983772278},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.49603065848350525},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.47452297806739807},{"id":"https://openalex.org/keywords/cross-validation","display_name":"Cross-validation","score":0.4487912058830261},{"id":"https://openalex.org/keywords/term","display_name":"Term (time)","score":0.4459781348705292},{"id":"https://openalex.org/keywords/feature-engineering","display_name":"Feature engineering","score":0.41342002153396606},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.41244786977767944},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.40657132863998413},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.1003744900226593},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.09438523650169373},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.09158283472061157},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.08651295304298401}],"concepts":[{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.9792475700378418},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7715953588485718},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7400550842285156},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6558682918548584},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5269815921783447},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5079815983772278},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.49603065848350525},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.47452297806739807},{"id":"https://openalex.org/C27181475","wikidata":"https://www.wikidata.org/wiki/Q541014","display_name":"Cross-validation","level":2,"score":0.4487912058830261},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.4459781348705292},{"id":"https://openalex.org/C2778827112","wikidata":"https://www.wikidata.org/wiki/Q22245680","display_name":"Feature engineering","level":3,"score":0.41342002153396606},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.41244786977767944},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.40657132863998413},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.1003744900226593},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.09438523650169373},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.09158283472061157},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.08651295304298401},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3340555.3355717","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3340555.3355717","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 International Conference on Multimodal Interaction","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":11,"referenced_works":["https://openalex.org/W2169570446","https://openalex.org/W2517194566","https://openalex.org/W2607167007","https://openalex.org/W2803089144","https://openalex.org/W2805937758","https://openalex.org/W2883437835","https://openalex.org/W2894553678","https://openalex.org/W2895475421","https://openalex.org/W2963524571","https://openalex.org/W2963962355","https://openalex.org/W2964054038"],"related_works":["https://openalex.org/W4362597605","https://openalex.org/W1574414179","https://openalex.org/W3099765033","https://openalex.org/W2801469686","https://openalex.org/W2149651625","https://openalex.org/W3094256312","https://openalex.org/W1983416467","https://openalex.org/W3034267371","https://openalex.org/W1800458610","https://openalex.org/W2384527366"],"abstract_inverted_index":{"This":[0],"paper":[1],"proposes":[2],"a":[3,32,52,66,77,85,94,109,121,133,153,186],"novel":[4],"engagement":[5,29,49,106,201],"intensity":[6,50],"prediction":[7],"approach,":[8],"which":[9],"is":[10,25,35,125],"also":[11],"applied":[12],"in":[13,20,40,65,108,132,229],"the":[14,28,48,105,140,148,161,166,172,177,196,200,214,219,226,230,237],"EmotiW":[15],"Challenge":[16],"2019":[17],"and":[18,43,61,71,93,102,118,143,198],"resulted":[19],"good":[21],"performance.":[22],"The":[23],"task":[24],"to":[26,100,127,159,170,176,194,212],"predict":[27,104],"level":[30,107],"when":[31],"subject":[33],"student":[34],"watching":[36],"an":[37],"educational":[38],"video":[39,111],"diverse":[41],"conditions":[42],"various":[44,113],"environments.":[45],"Assuming":[46],"that":[47],"has":[51],"strong":[53],"correlation":[54],"with":[55,84,112,190,206,232],"facial":[56],"movements,":[57],"upper-body":[58],"posture":[59],"movements":[60,64],"overall":[62],"environmental":[63],"time":[67,136],"interval,":[68],"we":[69,151,181],"extract":[70,128],"incorporate":[72],"these":[73],"motion":[74],"features":[75,131],"into":[76],"deep":[78],"regression":[79],"model":[80,167],"consisting":[81],"of":[82,87,135,234],"layers":[83],"combination":[86],"LSTM,":[88],"Gated":[89],"Recurrent":[90],"Unit":[91],"(GRU)":[92],"Fully":[95],"Connected":[96],"Layer.":[97],"In":[98],"order":[99],"precisely":[101],"robustly":[103],"long":[110],"situations":[114],"such":[115],"as":[116],"darkness":[117],"complex":[119],"background,":[120],"multi-features":[122],"engineering":[123],"method":[124],"used":[126],"synchronized":[129],"multi-model":[130],"period":[134],"by":[137],"considering":[138],"both":[139],"short-term":[141],"dependencies":[142],"long-term":[144],"dependencies.":[145],"Based":[146],"on":[147,236],"well-processed":[149],"features,":[150],"propose":[152,182],"strategy":[154,184],"for":[155],"maximizing":[156],"validation":[157,209],"accuracy":[158],"generate":[160],"best":[162],"models":[163],"covering":[164],"all":[165],"configurations.":[168],"Furthermore,":[169],"avoid":[171],"overfitting":[173],"problem":[174],"ascribed":[175],"extremely":[178],"small":[179],"database,":[180],"another":[183],"applying":[185],"single":[187],"Bi-LSTM":[188],"layer":[189],"only":[191],"16":[192],"units":[193],"minimize":[195],"overfitting,":[197],"splitting":[199],"dataset":[202],"(train":[203],"+":[204],"validation)":[205],"5-fold":[207],"cross":[208],"(stratified":[210],"k-fold)":[211],"train":[213],"conservative":[215],"model.":[216],"By":[217],"ensembling":[218],"above":[220],"models,":[221],"our":[222],"methods":[223],"finally":[224],"win":[225],"second":[227],"place":[228],"challenge":[231],"MSE":[233],"0.06174":[235],"testing":[238],"set.":[239]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1}],"updated_date":"2026-02-27T16:54:17.756197","created_date":"2025-10-10T00:00:00"}
