{"id":"https://openalex.org/W4416251533","doi":"https://doi.org/10.1109/ijcnn64981.2025.11227212","title":"AW-GATCN: Adaptive Weighted Graph Attention Convolutional Network for Event Camera Data Joint Denoising and Object Recognition","display_name":"AW-GATCN: Adaptive Weighted Graph Attention Convolutional Network for Event Camera Data Joint Denoising and Object Recognition","publication_year":2025,"publication_date":"2025-06-30","ids":{"openalex":"https://openalex.org/W4416251533","doi":"https://doi.org/10.1109/ijcnn64981.2025.11227212"},"language":null,"primary_location":{"id":"doi:10.1109/ijcnn64981.2025.11227212","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11227212","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","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/A5101574415","display_name":"Haiyu Li","orcid":"https://orcid.org/0000-0002-2097-1061"},"institutions":[{"id":"https://openalex.org/I91136226","display_name":"University of Sheffield","ror":"https://ror.org/05krs5044","country_code":"GB","type":"education","lineage":["https://openalex.org/I91136226"]}],"countries":["GB"],"is_corresponding":true,"raw_author_name":"Haiyu Li","raw_affiliation_strings":["The University of Sheffield,School of Electronic and Electrical Engineering,Sheffield,United Kingdom"],"affiliations":[{"raw_affiliation_string":"The University of Sheffield,School of Electronic and Electrical Engineering,Sheffield,United Kingdom","institution_ids":["https://openalex.org/I91136226"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5035952586","display_name":"Charith Abhayaratne","orcid":"https://orcid.org/0000-0002-2799-7395"},"institutions":[{"id":"https://openalex.org/I91136226","display_name":"University of Sheffield","ror":"https://ror.org/05krs5044","country_code":"GB","type":"education","lineage":["https://openalex.org/I91136226"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Charith Abhayaratne","raw_affiliation_strings":["The University of Sheffield,School of Electronic and Electrical Engineering,Centre for Machine Intelligence,Sheffield,United Kingdom"],"affiliations":[{"raw_affiliation_string":"The University of Sheffield,School of Electronic and Electrical Engineering,Centre for Machine Intelligence,Sheffield,United Kingdom","institution_ids":["https://openalex.org/I91136226"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5101574415"],"corresponding_institution_ids":["https://openalex.org/I91136226"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.36537583,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10502","display_name":"Advanced Memory and Neural Computing","score":0.8676999807357788,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10502","display_name":"Advanced Memory and Neural Computing","score":0.8676999807357788,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12808","display_name":"Ferroelectric and Negative Capacitance Devices","score":0.03220000118017197,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11181","display_name":"Advanced Data Storage Technologies","score":0.00839999970048666,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/noise-reduction","display_name":"Noise reduction","score":0.6840000152587891},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.5634999871253967},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5425000190734863},{"id":"https://openalex.org/keywords/event","display_name":"Event (particle physics)","score":0.5360999703407288},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5245000123977661},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.5175999999046326},{"id":"https://openalex.org/keywords/cognitive-neuroscience-of-visual-object-recognition","display_name":"Cognitive neuroscience of visual object recognition","score":0.42309999465942383},{"id":"https://openalex.org/keywords/joint","display_name":"Joint (building)","score":0.3828999996185303}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.738099992275238},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7285000085830688},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.6840000152587891},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.5634999871253967},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5425000190734863},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.5360999703407288},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5245000123977661},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.5175999999046326},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4959999918937683},{"id":"https://openalex.org/C64876066","wikidata":"https://www.wikidata.org/wiki/Q5141226","display_name":"Cognitive neuroscience of visual object recognition","level":3,"score":0.42309999465942383},{"id":"https://openalex.org/C18555067","wikidata":"https://www.wikidata.org/wiki/Q8375051","display_name":"Joint (building)","level":2,"score":0.3828999996185303},{"id":"https://openalex.org/C29265498","wikidata":"https://www.wikidata.org/wiki/Q7047719","display_name":"Noise measurement","level":3,"score":0.3682999908924103},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.3666999936103821},{"id":"https://openalex.org/C2781170535","wikidata":"https://www.wikidata.org/wiki/Q30587856","display_name":"Noisy data","level":2,"score":0.3644999861717224},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.364300012588501},{"id":"https://openalex.org/C125245961","wikidata":"https://www.wikidata.org/wiki/Q221656","display_name":"Brightness","level":2,"score":0.3562999963760376},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.32330000400543213},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.30889999866485596},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.305400013923645},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.29910001158714294},{"id":"https://openalex.org/C2983327147","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Image denoising","level":3,"score":0.2879999876022339},{"id":"https://openalex.org/C30814859","wikidata":"https://www.wikidata.org/wiki/Q4119603","display_name":"Video denoising","level":5,"score":0.2768000066280365},{"id":"https://openalex.org/C102248274","wikidata":"https://www.wikidata.org/wiki/Q168388","display_name":"Adaptive filter","level":2,"score":0.271699994802475}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn64981.2025.11227212","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11227212","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","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":15,"referenced_works":["https://openalex.org/W2012842163","https://openalex.org/W2020096355","https://openalex.org/W2108467139","https://openalex.org/W2207801947","https://openalex.org/W2469278928","https://openalex.org/W2619510810","https://openalex.org/W2922107638","https://openalex.org/W2955504279","https://openalex.org/W2963182550","https://openalex.org/W2981539886","https://openalex.org/W3034657761","https://openalex.org/W3040838455","https://openalex.org/W3092424719","https://openalex.org/W3122048951","https://openalex.org/W4360584268"],"related_works":[],"abstract_inverted_index":{"Event":[0],"cameras,":[1],"which":[2],"capture":[3],"brightness":[4],"changes":[5],"with":[6,136],"high":[7],"temporal":[8],"resolution,":[9],"inherently":[10],"generate":[11],"a":[12,70],"significant":[13],"amount":[14],"of":[15,85,113,141],"redundant":[16],"and":[17,74,117,127],"noisy":[18],"data":[19],"beyond":[20],"essential":[21],"object":[22,29],"structures.":[23],"The":[24],"primary":[25],"challenge":[26],"in":[27,32],"event-based":[28],"recognition":[30,111],"lies":[31],"effectively":[33,88],"removing":[34],"this":[35],"noise":[36,90,129],"without":[37],"losing":[38],"critical":[39,93],"spatial-temporal":[40],"information.":[41],"To":[42],"address":[43],"this,":[44],"we":[45],"propose":[46],"an":[47,137],"Adaptive":[48],"Graph-based":[49],"Noisy":[50],"Data":[51],"Removal":[52],"framework":[53],"for":[54,96],"Event-based":[55],"Object":[56],"Recognition.":[57],"Specifically,":[58],"our":[59,107],"approach":[60],"integrates":[61],"adaptive":[62,75],"event":[63],"segmentation":[64],"based":[65],"on":[66,101],"normalized":[67],"density":[68],"analysis,":[69],"multifactorial":[71],"edge-weighting":[72],"mechanism,":[73],"graph-based":[76,121],"denoising":[77],"strategies.":[78],"These":[79],"innovations":[80],"significantly":[81],"enhance":[82],"the":[83],"integration":[84],"spatiotemporal":[86],"information,":[87],"filtering":[89],"while":[91],"preserving":[92],"structural":[94],"features":[95],"robust":[97],"recognition.":[98],"Experimental":[99],"evaluations":[100],"four":[102],"challenging":[103],"datasets":[104],"demonstrate":[105],"that":[106],"method":[108],"achieves":[109],"superior":[110],"accuracies":[112],"83.77%,":[114],"76.79%,":[115],"99.30%,":[116],"96.89%,":[118],"surpassing":[119],"existing":[120],"methods":[122],"by":[123,132],"up":[124,133],"to":[125,134,144],"8.79%,":[126],"improving":[128],"reduction":[130],"performance":[131],"19.57%,":[135],"additional":[138],"accuracy":[139],"gain":[140],"6.26%":[142],"compared":[143],"traditional":[145],"Euclidean-based":[146],"techniques.":[147]},"counts_by_year":[],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2025-11-14T00:00:00"}