{"id":"https://openalex.org/W7153174739","doi":"https://doi.org/10.48550/arxiv.2604.08106","title":"EPIR: An Efficient Patch Tokenization, Integration and Representation Framework for Micro-expression Recognition","display_name":"EPIR: An Efficient Patch Tokenization, Integration and Representation Framework for Micro-expression Recognition","publication_year":2026,"publication_date":"2026-04-09","ids":{"openalex":"https://openalex.org/W7153174739","doi":"https://doi.org/10.48550/arxiv.2604.08106"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.08106","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.08106","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.2604.08106","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5133364266","display_name":"Junbo Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Junbo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5019642277","display_name":"Liangyu Fu","orcid":"https://orcid.org/0009-0006-6433-7528"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fu, Liangyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133362126","display_name":"Yuke Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Yuke","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5099093361","display_name":"Yining Zhu","orcid":"https://orcid.org/0009-0006-6003-7739"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhu, Yining","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5108497986","display_name":"Xuecheng Wu","orcid":"https://orcid.org/0000-0002-6244-0269"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wu, Xuecheng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5133348061","display_name":"Kun Hu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hu, Kun","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/T10667","display_name":"Emotion and Mood Recognition","score":0.9675999879837036,"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"}},"topics":[{"id":"https://openalex.org/T10667","display_name":"Emotion and Mood Recognition","score":0.9675999879837036,"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/T11448","display_name":"Face recognition and analysis","score":0.014800000004470348,"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/T10057","display_name":"Face and Expression Recognition","score":0.003000000026077032,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.7179999947547913},{"id":"https://openalex.org/keywords/security-token","display_name":"Security token","score":0.6014999747276306},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.39730000495910645},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3479999899864197},{"id":"https://openalex.org/keywords/block","display_name":"Block (permutation group theory)","score":0.3449999988079071},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.3068000078201294},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.2989000082015991},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.2953999936580658},{"id":"https://openalex.org/keywords/transformation","display_name":"Transformation (genetics)","score":0.29350000619888306}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8043000102043152},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.7179999947547913},{"id":"https://openalex.org/C48145219","wikidata":"https://www.wikidata.org/wiki/Q1335365","display_name":"Security token","level":2,"score":0.6014999747276306},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5392000079154968},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.39730000495910645},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.392300009727478},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3479999899864197},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.3449999988079071},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.3068000078201294},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.2989000082015991},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2953999936580658},{"id":"https://openalex.org/C204241405","wikidata":"https://www.wikidata.org/wiki/Q461499","display_name":"Transformation (genetics)","level":3,"score":0.29350000619888306},{"id":"https://openalex.org/C31510193","wikidata":"https://www.wikidata.org/wiki/Q1192553","display_name":"Facial recognition system","level":3,"score":0.2888000011444092},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.2858000099658966},{"id":"https://openalex.org/C2780980858","wikidata":"https://www.wikidata.org/wiki/Q110022","display_name":"Dual (grammatical number)","level":2,"score":0.28209999203681946},{"id":"https://openalex.org/C179799912","wikidata":"https://www.wikidata.org/wiki/Q205084","display_name":"Computational complexity theory","level":2,"score":0.27810001373291016},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.2768999934196472},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.2768000066280365},{"id":"https://openalex.org/C77246614","wikidata":"https://www.wikidata.org/wiki/Q1409400","display_name":"Gramian matrix","level":3,"score":0.2718000113964081},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.2655999958515167},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.2621000111103058},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.26019999384880066},{"id":"https://openalex.org/C2984118289","wikidata":"https://www.wikidata.org/wiki/Q29954","display_name":"Power consumption","level":3,"score":0.25380000472068787},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2515000104904175},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.2506999969482422}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.08106","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.08106","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.2604.08106","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.08106","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":[{"id":"https://metadata.un.org/sdg/10","score":0.752632737159729,"display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Micro-expression":[0],"recognition":[1,79],"can":[2,76],"obtain":[3],"the":[4,8,11,33,39,44,98,105,138,155,158,163,167,174,218,226,237],"real":[5],"emotion":[6],"of":[7,36,140,166,231,242],"individual":[9],"at":[10],"current":[12],"moment.":[13],"Although":[14],"deep":[15],"learning-based":[16],"methods,":[17,20,220],"especially":[18],"Transformer-based":[19,55],"have":[21,27],"achieved":[22],"impressive":[23],"results,":[24],"these":[25],"methods":[26],"high":[28,78],"computational":[29,83],"complexity":[30],"due":[31],"to":[32,57,96,127,161,180],"large":[34],"number":[35,139],"tokens":[37,130,141],"in":[38,104,157,229,240],"multi-head":[40],"self-attention.":[41],"In":[42],"addition,":[43],"existing":[45],"micro-expression":[46,60,188],"datasets":[47,198],"are":[48],"small-scale,":[49],"which":[50,75,108,152],"makes":[51],"it":[52],"difficult":[53],"for":[54],"models":[56],"learn":[58,97],"effective":[59],"representations.":[61,189],"Therefore,":[62],"we":[63,86,121,146],"propose":[64,88,122],"a":[65,89,112,123,148],"novel":[66],"Efficient":[67],"Patch":[68],"tokenization,":[69],"Integration":[70],"and":[71,81,116,172,204,233],"Representation":[72],"framework":[73],"(EPIR),":[74],"balance":[77],"performance":[80,215],"low":[82],"complexity.":[84],"Specifically,":[85],"first":[87,153],"dual":[90,117],"norm":[91,118],"shifted":[92],"tokenization":[93],"(DNSPT)":[94],"module":[95,126,178],"spatial":[99,114],"relationship":[100],"between":[101],"neighboring":[102],"pixels":[103],"face":[106],"region,":[107],"is":[109],"implemented":[110],"by":[111],"refined":[113],"transformation":[115],"projection.":[119],"Then,":[120],"token":[124,150,176],"integration":[125],"integrate":[128],"partial":[129],"among":[131],"multiple":[132],"cascaded":[133],"Transformer":[134,159],"blocks,":[135],"thereby":[136,184],"reducing":[137],"without":[142],"information":[143],"loss.":[144],"Furthermore,":[145],"design":[147],"discriminative":[149,187],"extractor,":[151],"improves":[154],"attention":[156,168],"block":[160],"reduce":[162],"unnecessary":[164],"focus":[165],"calculation":[169],"on":[170,194,225,236],"self-tokens,":[171],"uses":[173],"dynamic":[175],"selection":[177],"(DTSM)":[179],"select":[181],"key":[182],"tokens,":[183],"capturing":[185],"more":[186],"We":[190],"conduct":[191],"extensive":[192],"experiments":[193],"four":[195],"popular":[196],"public":[197],"(i.e.,":[199],"CASME":[200],"II,":[201],"SAMM,":[202],"SMIC,":[203],"CAS(ME)3.":[205],"The":[206],"experimental":[207],"results":[208],"show":[209],"that":[210],"our":[211],"method":[212],"achieves":[213],"significant":[214],"gains":[216],"over":[217],"state-of-the-art":[219],"such":[221],"as":[222],"9.6%":[223],"improvement":[224,235],"CAS(ME)$^3$":[227],"dataset":[228,239],"terms":[230,241],"UF1":[232],"4.58%":[234],"SMIC":[238],"UAR":[243],"metric.":[244]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-04-11T00:00:00"}
