{"id":"https://openalex.org/W4415537500","doi":"https://doi.org/10.1145/3746027.3762026","title":"Boosting Micro-Expression Analysis via Prior-Guided Video-Level Regression","display_name":"Boosting Micro-Expression Analysis via Prior-Guided Video-Level Regression","publication_year":2025,"publication_date":"2025-10-25","ids":{"openalex":"https://openalex.org/W4415537500","doi":"https://doi.org/10.1145/3746027.3762026"},"language":null,"primary_location":{"id":"doi:10.1145/3746027.3762026","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3746027.3762026","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 33rd ACM International Conference on Multimedia","raw_type":"proceedings-article"},"type":"conference-paper","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/A5101159557","display_name":"Zizheng Guo","orcid":null},"institutions":[{"id":"https://openalex.org/I92403157","display_name":"University of Science and Technology Beijing","ror":"https://ror.org/02egmk993","country_code":"CN","type":"education","lineage":["https://openalex.org/I92403157"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zizheng Guo","raw_affiliation_strings":["University of Science and Technology, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0005-0319-8344","affiliations":[{"raw_affiliation_string":"University of Science and Technology, Beijing, China","institution_ids":["https://openalex.org/I92403157"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5045750357","display_name":"Bochao Zou","orcid":"https://orcid.org/0000-0002-2126-8159"},"institutions":[{"id":"https://openalex.org/I92403157","display_name":"University of Science and Technology Beijing","ror":"https://ror.org/02egmk993","country_code":"CN","type":"education","lineage":["https://openalex.org/I92403157"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bochao Zou","raw_affiliation_strings":["University of Science and Technology, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-2126-8159","affiliations":[{"raw_affiliation_string":"University of Science and Technology, Beijing, China","institution_ids":["https://openalex.org/I92403157"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Yinuo Jia","orcid":"https://orcid.org/0009-0007-0181-4187"},"institutions":[{"id":"https://openalex.org/I92403157","display_name":"University of Science and Technology Beijing","ror":"https://ror.org/02egmk993","country_code":"CN","type":"education","lineage":["https://openalex.org/I92403157"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yinuo Jia","raw_affiliation_strings":["University of Science and Technology, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0007-0181-4187","affiliations":[{"raw_affiliation_string":"University of Science and Technology, Beijing, China","institution_ids":["https://openalex.org/I92403157"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Xiangyu Li","orcid":"https://orcid.org/0009-0004-4786-636X"},"institutions":[{"id":"https://openalex.org/I92403157","display_name":"University of Science and Technology Beijing","ror":"https://ror.org/02egmk993","country_code":"CN","type":"education","lineage":["https://openalex.org/I92403157"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiangyu Li","raw_affiliation_strings":["University of Science and Technology, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0004-4786-636X","affiliations":[{"raw_affiliation_string":"University of Science and Technology, Beijing, China","institution_ids":["https://openalex.org/I92403157"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5006236325","display_name":"Huimin Ma","orcid":"https://orcid.org/0000-0001-5383-5667"},"institutions":[{"id":"https://openalex.org/I92403157","display_name":"University of Science and Technology Beijing","ror":"https://ror.org/02egmk993","country_code":"CN","type":"education","lineage":["https://openalex.org/I92403157"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Huimin Ma","raw_affiliation_strings":["University of Science and Technology, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0001-5383-5667","affiliations":[{"raw_affiliation_string":"University of Science and Technology, Beijing, China","institution_ids":["https://openalex.org/I92403157"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I92403157"],"apc_list":null,"apc_paid":null,"fwci":1.4136,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.8480039,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"13964","last_page":"13971"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10812","display_name":"Human Pose and Action Recognition","score":0.9997000098228455,"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":0.9997000098228455,"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":0.9976000189781189,"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":0.996399998664856,"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/boosting","display_name":"Boosting (machine learning)","score":0.7080000042915344},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.5037000179290771},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.4431999921798706},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.44290000200271606},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.4352000057697296},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.40619999170303345},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.37380000948905945},{"id":"https://openalex.org/keywords/regression-analysis","display_name":"Regression analysis","score":0.36880001425743103}],"concepts":[{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.7080000042915344},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6894999742507935},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5659999847412109},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.5037000179290771},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5012999773025513},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.4431999921798706},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.44290000200271606},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.4352000057697296},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.40619999170303345},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.39890000224113464},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.37380000948905945},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.36880001425743103},{"id":"https://openalex.org/C175291020","wikidata":"https://www.wikidata.org/wiki/Q1156822","display_name":"Offset (computer science)","level":2,"score":0.36090001463890076},{"id":"https://openalex.org/C2778067643","wikidata":"https://www.wikidata.org/wiki/Q166507","display_name":"Interval (graph theory)","level":2,"score":0.3407999873161316},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.335099995136261},{"id":"https://openalex.org/C2779506182","wikidata":"https://www.wikidata.org/wiki/Q7580141","display_name":"Spotting","level":2,"score":0.3319000005722046},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.3253999948501587},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.31700000166893005},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.30410000681877136},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.2815999984741211},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.2782000005245209},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2678000032901764},{"id":"https://openalex.org/C127705205","wikidata":"https://www.wikidata.org/wiki/Q5748245","display_name":"Heuristics","level":2,"score":0.2621000111103058},{"id":"https://openalex.org/C2984074130","wikidata":"https://www.wikidata.org/wiki/Q73539779","display_name":"R package","level":2,"score":0.2524999976158142}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3746027.3762026","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3746027.3762026","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 33rd ACM International Conference on Multimedia","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2134536453","display_name":null,"funder_award_id":"62206015","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":44,"referenced_works":["https://openalex.org/W2048244171","https://openalex.org/W2093033615","https://openalex.org/W2097290407","https://openalex.org/W2128730107","https://openalex.org/W2139916508","https://openalex.org/W2196663747","https://openalex.org/W2241236839","https://openalex.org/W2426188534","https://openalex.org/W2478411578","https://openalex.org/W2526853616","https://openalex.org/W2567449792","https://openalex.org/W2899883316","https://openalex.org/W2911617386","https://openalex.org/W2956178895","https://openalex.org/W2959679536","https://openalex.org/W2963230974","https://openalex.org/W2964097128","https://openalex.org/W2990503944","https://openalex.org/W3080756971","https://openalex.org/W3128209522","https://openalex.org/W3128621833","https://openalex.org/W3128811047","https://openalex.org/W3128946893","https://openalex.org/W3132823167","https://openalex.org/W3141012605","https://openalex.org/W3162395899","https://openalex.org/W3194570764","https://openalex.org/W3195543922","https://openalex.org/W3206718318","https://openalex.org/W4214612132","https://openalex.org/W4280528148","https://openalex.org/W4306759110","https://openalex.org/W4312399808","https://openalex.org/W4365790465","https://openalex.org/W4386075510","https://openalex.org/W4387760641","https://openalex.org/W4387968479","https://openalex.org/W4388347827","https://openalex.org/W4389611070","https://openalex.org/W4390607209","https://openalex.org/W4403791392","https://openalex.org/W4403791413","https://openalex.org/W4407694813","https://openalex.org/W4408352329"],"related_works":[],"abstract_inverted_index":{"Micro-expressions":[0],"(MEs)":[1],"are":[2],"involuntary,":[3],"low-intensity,":[4],"and":[5,16,31,102,115,130,152,177],"short-duration":[6],"facial":[7],"expressions":[8],"that":[9,95],"often":[10],"reveal":[11],"an":[12,171],"individual's":[13],"genuine":[14],"thoughts":[15],"emotions.":[17],"Most":[18],"existing":[19],"ME":[20,86],"analysis":[21],"methods":[22],"rely":[23],"on":[24,64,159,175,179],"window-level":[25],"classification":[26,138],"with":[27,170],"fixed":[28],"window":[29],"sizes":[30],"hard":[32],"decisions,":[33],"which":[34,127],"limits":[35],"their":[36],"ability":[37],"to":[38,54],"capture":[39],"the":[40,70,98,112,128,137,154,164],"complex":[41],"temporal":[42,99],"dynamics":[43],"of":[44,57,106,111,149,167,173],"MEs.":[45],"Although":[46],"recent":[47],"approaches":[48],"have":[49],"adopted":[50],"video-level":[51,82],"regression":[52,83],"frameworks":[53],"address":[55],"some":[56],"these":[58],"challenges,":[59],"interval":[60,92],"decoding":[61],"still":[62],"depends":[63],"manually":[65],"predefined,":[66],"window-based":[67],"methods,":[68],"leaving":[69],"issue":[71],"only":[72],"partially":[73],"mitigated.":[74],"In":[75,118],"this":[76],"paper,":[77],"we":[78,120],"propose":[79],"a":[80,90,122],"prior-guided":[81],"method":[84],"for":[85,136],"analysis.":[87],"We":[88],"introduce":[89,121],"scalable":[91],"selection":[93],"strategy":[94],"comprehensively":[96],"considers":[97],"evolution,":[100],"duration,":[101],"class":[103],"distribution":[104],"characteristics":[105],"MEs,":[107],"enabling":[108],"precise":[109],"spotting":[110,129],"onset,":[113],"apex,":[114],"offset":[116],"phases.":[117],"addition,":[119],"synergistic":[123],"optimization":[124],"framework,":[125],"in":[126],"recognition":[131],"tasks":[132],"share":[133],"parameters":[134],"except":[135],"heads.":[139],"This":[140],"fully":[141],"exploits":[142],"complementary":[143],"information,":[144],"makes":[145],"more":[146],"efficient":[147],"use":[148],"limited":[150],"data,":[151],"enhances":[153],"model's":[155],"capability.":[156],"Extensive":[157],"experiments":[158],"multiple":[160],"benchmark":[161],"datasets":[162],"demonstrate":[163],"state-of-the-art":[165],"performance":[166],"our":[168],"method,":[169],"STRS":[172],"0.0562":[174],"CAS(ME)3":[176],"0.2000":[178],"SAMMLV.":[180],"The":[181],"code":[182],"is":[183],"available":[184],"at":[185],"https://github.com/zizheng-guo/BoostingVRME.":[186]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-25T00:00:00"}
