{"id":"https://openalex.org/W4404629036","doi":"https://doi.org/10.1109/ipta62886.2024.10755929","title":"A Comparative Study: Error Analysis and Model Efficiency in Event-Based Action Recognition","display_name":"A Comparative Study: Error Analysis and Model Efficiency in Event-Based Action Recognition","publication_year":2024,"publication_date":"2024-10-14","ids":{"openalex":"https://openalex.org/W4404629036","doi":"https://doi.org/10.1109/ipta62886.2024.10755929"},"language":"en","primary_location":{"id":"doi:10.1109/ipta62886.2024.10755929","doi":"https://doi.org/10.1109/ipta62886.2024.10755929","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/ipta62886.2024.10755929","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE Thirteenth International Conference on Image Processing Theory, Tools and Applications (IPTA)","raw_type":"proceedings-article"},"volume":null,"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/A5114755104","display_name":"Mira Adra","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I1902872","display_name":"EURECOM","ror":"https://ror.org/00sse7z02","country_code":"FR","type":"education","lineage":["https://openalex.org/I1902872","https://openalex.org/I205703379"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Mira Adra","raw_affiliation_strings":["Eurecom,Biot,France"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Eurecom,Biot,France","institution_ids":["https://openalex.org/I1902872"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5055361726","display_name":"Jean\u2010Luc Dugelay","orcid":"https://orcid.org/0000-0003-3151-4330","observed_orcids":["https://orcid.org/0000-0003-3151-4330"]},"institutions":[{"id":"https://openalex.org/I1902872","display_name":"EURECOM","ror":"https://ror.org/00sse7z02","country_code":"FR","type":"education","lineage":["https://openalex.org/I1902872","https://openalex.org/I205703379"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Jean\u2013Luc Dugelay","raw_affiliation_strings":["Eurecom,Biot,France"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Eurecom,Biot,France","institution_ids":["https://openalex.org/I1902872"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5025618391","display_name":"C\u00e9cile Ichard","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cecile Ichard","raw_affiliation_strings":["GTD International,Toulouse,France"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"GTD International,Toulouse,France","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.2291,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.54825794,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"01","last_page":"06"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10812","display_name":"Human Pose and Action Recognition","score":0.9824000000953674,"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":0.9824000000953674,"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/T10036","display_name":"Advanced Neural Network Applications","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/T11398","display_name":"Hand Gesture Recognition Systems","score":0.0035000001080334187,"subfield":{"id":"https://openalex.org/subfields/1709","display_name":"Human-Computer Interaction"},"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/error-analysis","display_name":"error analysis","score":0.8349999785423279},{"id":"https://openalex.org/keywords/spiking-neural-networks","display_name":"spiking neural networks","score":0.8370000123977661},{"id":"https://openalex.org/keywords/graph-convolutional-networks","display_name":"graph convolutional networks","score":0.9150000214576721},{"id":"https://openalex.org/keywords/video-transformer","display_name":"video transformer","score":0.7630000114440918},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"ensemble learning","score":0.953000009059906},{"id":"https://openalex.org/keywords/model-efficiency","display_name":"model efficiency","score":0.4729999899864197},{"id":"https://openalex.org/keywords/model-selection","display_name":"model selection","score":0.5630000233650208}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7038390040397644},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.4707399308681488},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4610741436481476},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.46015575528144836},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.4547847509384155},{"id":"https://openalex.org/C3018824978","wikidata":"https://www.wikidata.org/wiki/Q2894891","display_name":"Error analysis","level":2,"score":0.4342806339263916},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1644705832004547},{"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/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ipta62886.2024.10755929","doi":"https://doi.org/10.1109/ipta62886.2024.10755929","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/ipta62886.2024.10755929","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE Thirteenth International Conference on Image Processing Theory, Tools and Applications (IPTA)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"sustainable_development_goals_aurora":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W3188962172","https://openalex.org/W2772917594","https://openalex.org/W4312825515","https://openalex.org/W4306742369","https://openalex.org/W4303457083","https://openalex.org/W2131146434","https://openalex.org/W2951359407","https://openalex.org/W2043727559","https://openalex.org/W4376623224","https://openalex.org/W3204019825"],"abstract_inverted_index":{"In":[0],"the":[1,15,54,106,117,135,143,150,159],"domain":[2],"of":[3,17,57,78,114,161],"event-based":[4,174],"vision,":[5],"action":[6],"recognition":[7],"stands":[8],"as":[9],"a":[10,167],"significant":[11],"challenge":[12,149],"that":[13,66,116],"pushes":[14],"boundaries":[16],"advanced":[18],"computational":[19],"models.":[20,184],"This":[21,122,164],"paper":[22],"compares":[23],"three":[24],"cutting-edge":[25],"architectures":[26],"-":[27,39],"Spiking":[28,118,144],"Neural":[29,33,119],"Networks,":[30,34],"Graph":[31],"Convolutional":[32],"and":[35,60,141,177],"Video":[36,107],"Transformer-based":[37],"Networks":[38],"to":[40,74,88,138,181],"determine":[41],"their":[42,90],"effectiveness":[43],"in":[44,124,173],"this":[45,82],"domain.":[46],"Our":[47,147],"study":[48],"extends":[49],"beyond":[50],"accuracy,":[51],"focusing":[52],"on":[53,81,112,130,153],"error":[55,84,162],"nature":[56],"each":[58],"model":[59,156,171],"its":[61],"corresponding":[62],"complexity.":[63],"We":[64],"observed":[65],"these":[67,183],"models":[68],"while":[69],"achieving":[70],"comparable":[71],"accuracies,":[72],"tend":[73],"make":[75],"different":[76],"types":[77],"mistakes.":[79],"Capitalizing":[80],"comple-mentary":[83],"phenomenon,":[85],"we":[86,103],"aim":[87],"leverage":[89],"strengths":[91],"by":[92],"proposing":[93],"an":[94],"ensemble":[95],"learning":[96],"strategy,":[97],"which":[98],"improves":[99],"overall":[100],"performance.":[101],"Moreover,":[102],"further":[104],"investigated":[105],"Trans-former":[108],"model,":[109],"retraining":[110],"it":[111],"subsets":[113,131],"data":[115],"Network":[120],"misclassified.":[121],"resulted":[123],"higher":[125],"accuracy":[126,154],"than":[127],"when":[128],"trained":[129],"correctly":[132],"classified,":[133],"highlighting":[134],"Transformer's":[136],"ability":[137],"learn":[139],"differently":[140],"complement":[142],"Network's":[145],"weaknesses.":[146],"findings":[148],"sole":[151],"focus":[152],"for":[155,170],"efficacy":[157],"emphasizing":[158],"significance":[160],"analysis.":[163],"research":[165],"provides":[166],"road":[168],"map":[169],"selection":[172],"vision":[175],"tasks":[176],"introduces":[178],"innovative":[179],"ways":[180],"integrate":[182]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
