{"id":"https://openalex.org/W4387486242","doi":"https://doi.org/10.23919/spa59660.2023.10274464","title":"Memory-Efficient Graph Convolutional Networks for Object Classification and Detection with Event Cameras","display_name":"Memory-Efficient Graph Convolutional Networks for Object Classification and Detection with Event Cameras","publication_year":2023,"publication_date":"2023-09-20","ids":{"openalex":"https://openalex.org/W4387486242","doi":"https://doi.org/10.23919/spa59660.2023.10274464"},"language":"en","primary_location":{"id":"doi:10.23919/spa59660.2023.10274464","is_oa":false,"landing_page_url":"https://doi.org/10.23919/spa59660.2023.10274464","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA)","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/A5023081447","display_name":"Kamil Jeziorek","orcid":"https://orcid.org/0000-0001-5446-3682"},"institutions":[{"id":"https://openalex.org/I4210145666","display_name":"Embedded Systems (United States)","ror":"https://ror.org/04742eh45","country_code":"US","type":"company","lineage":["https://openalex.org/I4210145666"]},{"id":"https://openalex.org/I686019","display_name":"AGH University of Krakow","ror":"https://ror.org/00bas1c41","country_code":"PL","type":"education","lineage":["https://openalex.org/I686019"]}],"countries":["PL","US"],"is_corresponding":true,"raw_author_name":"Kamil Jeziorek","raw_affiliation_strings":["AGH University of Krakow,Embedded Vision Systems Group,Department of Automatic Control and Robotics,Poland","Department of Automatic Control and Robotics, Embedded Vision Systems Group, AGH University of Krakow, Poland"],"affiliations":[{"raw_affiliation_string":"AGH University of Krakow,Embedded Vision Systems Group,Department of Automatic Control and Robotics,Poland","institution_ids":["https://openalex.org/I4210145666","https://openalex.org/I686019"]},{"raw_affiliation_string":"Department of Automatic Control and Robotics, Embedded Vision Systems Group, AGH University of Krakow, Poland","institution_ids":["https://openalex.org/I686019"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5019342840","display_name":"Andr\u00e9a Pinna","orcid":"https://orcid.org/0000-0002-2388-7404"},"institutions":[{"id":"https://openalex.org/I1294671590","display_name":"Centre National de la Recherche Scientifique","ror":"https://ror.org/02feahw73","country_code":"FR","type":"government","lineage":["https://openalex.org/I1294671590"]},{"id":"https://openalex.org/I204730241","display_name":"Universit\u00e9 Paris Cit\u00e9","ror":"https://ror.org/05f82e368","country_code":"FR","type":"education","lineage":["https://openalex.org/I204730241"]},{"id":"https://openalex.org/I39804081","display_name":"Sorbonne Universit\u00e9","ror":"https://ror.org/02en5vm52","country_code":"FR","type":"education","lineage":["https://openalex.org/I39804081"]},{"id":"https://openalex.org/I4210159731","display_name":"LIP6","ror":"https://ror.org/05krcen59","country_code":"FR","type":"facility","lineage":["https://openalex.org/I1294671590","https://openalex.org/I1294671590","https://openalex.org/I39804081","https://openalex.org/I4210159245","https://openalex.org/I4210159731"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Andrea Pinna","raw_affiliation_strings":["Sorbonne Universite, CNRS, LIP6,Paris,France,F-75005"],"affiliations":[{"raw_affiliation_string":"Sorbonne Universite, CNRS, LIP6,Paris,France,F-75005","institution_ids":["https://openalex.org/I4210159731","https://openalex.org/I204730241","https://openalex.org/I1294671590","https://openalex.org/I39804081"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5005086061","display_name":"Tomasz Kryjak","orcid":"https://orcid.org/0000-0001-6798-4444"},"institutions":[{"id":"https://openalex.org/I1294671590","display_name":"Centre National de la Recherche Scientifique","ror":"https://ror.org/02feahw73","country_code":"FR","type":"government","lineage":["https://openalex.org/I1294671590"]},{"id":"https://openalex.org/I204730241","display_name":"Universit\u00e9 Paris Cit\u00e9","ror":"https://ror.org/05f82e368","country_code":"FR","type":"education","lineage":["https://openalex.org/I204730241"]},{"id":"https://openalex.org/I39804081","display_name":"Sorbonne Universit\u00e9","ror":"https://ror.org/02en5vm52","country_code":"FR","type":"education","lineage":["https://openalex.org/I39804081"]},{"id":"https://openalex.org/I4210145666","display_name":"Embedded Systems (United States)","ror":"https://ror.org/04742eh45","country_code":"US","type":"company","lineage":["https://openalex.org/I4210145666"]},{"id":"https://openalex.org/I686019","display_name":"AGH University of Krakow","ror":"https://ror.org/00bas1c41","country_code":"PL","type":"education","lineage":["https://openalex.org/I686019"]}],"countries":["FR","PL","US"],"is_corresponding":false,"raw_author_name":"Tomasz Kryjak","raw_affiliation_strings":["AGH University of Krakow,Embedded Vision Systems Group,Department of Automatic Control and Robotics,Poland","Department of Automatic Control and Robotics, Embedded Vision Systems Group, AGH University of Krakow, Poland","Sorbonne Universite, CNRS, LIP6, Paris, France"],"affiliations":[{"raw_affiliation_string":"AGH University of Krakow,Embedded Vision Systems Group,Department of Automatic Control and Robotics,Poland","institution_ids":["https://openalex.org/I4210145666","https://openalex.org/I686019"]},{"raw_affiliation_string":"Department of Automatic Control and Robotics, Embedded Vision Systems Group, AGH University of Krakow, Poland","institution_ids":["https://openalex.org/I686019"]},{"raw_affiliation_string":"Sorbonne Universite, CNRS, LIP6, Paris, France","institution_ids":["https://openalex.org/I204730241","https://openalex.org/I39804081","https://openalex.org/I1294671590"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5023081447"],"corresponding_institution_ids":["https://openalex.org/I4210145666","https://openalex.org/I686019"],"apc_list":null,"apc_paid":null,"fwci":0.9372,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.74946761,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"160","last_page":"165"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10502","display_name":"Advanced Memory and Neural Computing","score":0.9958999752998352,"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":0.9958999752998352,"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/T12386","display_name":"Advanced X-ray and CT Imaging","score":0.9883999824523926,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical 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/T11612","display_name":"Stochastic Gradient Optimization Techniques","score":0.9835000038146973,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8187706470489502},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5954535007476807},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5743958950042725},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5426188111305237},{"id":"https://openalex.org/keywords/latency","display_name":"Latency (audio)","score":0.5116827487945557},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.5004277229309082},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.48888152837753296},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.4635765850543976},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4498622715473175},{"id":"https://openalex.org/keywords/reduction","display_name":"Reduction (mathematics)","score":0.4109737277030945},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.34056252241134644},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.19749069213867188},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.16518843173980713}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8187706470489502},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5954535007476807},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5743958950042725},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5426188111305237},{"id":"https://openalex.org/C82876162","wikidata":"https://www.wikidata.org/wiki/Q17096504","display_name":"Latency (audio)","level":2,"score":0.5116827487945557},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.5004277229309082},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.48888152837753296},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.4635765850543976},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4498622715473175},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.4109737277030945},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.34056252241134644},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.19749069213867188},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.16518843173980713},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.23919/spa59660.2023.10274464","is_oa":false,"landing_page_url":"https://doi.org/10.23919/spa59660.2023.10274464","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W2020096355","https://openalex.org/W2135957668","https://openalex.org/W2560609797","https://openalex.org/W2606202972","https://openalex.org/W2768308213","https://openalex.org/W2907492528","https://openalex.org/W2962804204","https://openalex.org/W2964015378","https://openalex.org/W2979750740","https://openalex.org/W2979969178","https://openalex.org/W2981539886","https://openalex.org/W2998281665","https://openalex.org/W3040838455","https://openalex.org/W3087591564","https://openalex.org/W3090117981","https://openalex.org/W3090951784","https://openalex.org/W3202353648","https://openalex.org/W4288419263","https://openalex.org/W4294558607","https://openalex.org/W4309875759","https://openalex.org/W4312281374","https://openalex.org/W6745584861","https://openalex.org/W6747904511","https://openalex.org/W6763422710"],"related_works":["https://openalex.org/W4293226380","https://openalex.org/W4321487865","https://openalex.org/W4313906399","https://openalex.org/W4391266461","https://openalex.org/W2590798552","https://openalex.org/W2811106690","https://openalex.org/W4239306820","https://openalex.org/W2964954556","https://openalex.org/W2969228573","https://openalex.org/W2963690996"],"abstract_inverted_index":{"Recent":[0],"advances":[1],"in":[2,9,53,75,123,137,160],"event":[3,42],"camera":[4],"research":[5,52],"emphasize":[6],"processing":[7],"data":[8,43,111,142],"its":[10,19,175],"original":[11],"sparse":[12],"form,":[13],"which":[14,151],"allows":[15],"the":[16,63,105,124,129,138,141,157,169,178],"use":[17],"of":[18,94,107,126,140,149,186,194],"unique":[20],"features":[21],"such":[22,101],"as":[23,102],"high":[24,27],"temporal":[25],"resolution,":[26],"dynamic":[28],"range,":[29],"low":[30,83],"latency,":[31],"and":[32,81,114,133,173,189],"resistance":[33],"to":[34,77,156],"image":[35],"blur.":[36],"One":[37],"promising":[38],"approach":[39],"for":[40,128],"analyzing":[41],"is":[44,152],"through":[45],"graph":[46,96],"convolutional":[47],"networks":[48],"(GCNs).":[49],"However,":[50],"current":[51],"this":[54,68,87],"domain":[55],"primarily":[56],"focuses":[57],"on":[58,177],"optimizing":[59],"computational":[60],"costs,":[61],"neglecting":[62],"associated":[64],"memory":[65],"costs.":[66],"In":[67],"paper,":[69],"we":[70,89,167],"consider":[71],"both":[72],"factors":[73,100],"together":[74],"order":[76],"achieve":[78],"satisfying":[79],"results":[80,118,182],"relatively":[82],"model":[84,109],"complexity.":[85],"For":[86],"purpose,":[88],"performed":[90],"a":[91,120,134,146],"comparative":[92],"analysis":[93],"different":[95],"convolution":[97],"operations,":[98],"considering":[99],"execution":[103,192],"time,":[104],"number":[106,125],"trainable":[108],"parameters,":[110],"format":[112],"requirements,":[113],"training":[115],"outcomes.":[116],"Our":[117],"show":[119],"450-fold":[121],"reduction":[122,136],"parameters":[127],"feature":[130],"extraction":[131],"module":[132],"4.5-fold":[135],"size":[139],"representation":[143],"while":[144],"maintaining":[145],"classification":[147],"accuracy":[148,185],"52.3%,":[150],"6.3%":[153],"higher":[154],"compared":[155],"operation":[158],"used":[159],"state-of-the-art":[161],"approaches.":[162],"To":[163],"further":[164],"evaluate":[165],"performance,":[166],"implemented":[168],"object":[170],"detection":[171],"architecture":[172],"evaluated":[174],"performance":[176],"N-Caltech101":[179],"dataset.":[180],"The":[181],"showed":[183],"an":[184,191],"53.7%":[187],"mAP@0.5":[188],"reached":[190],"rate":[193],"82":[195],"graphs":[196],"per":[197],"second.":[198]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
