{"id":"https://openalex.org/W4402742457","doi":"https://doi.org/10.1109/tits.2024.3457583","title":"SeqNet: Sequential Networks for One-Shot Traffic Sign Recognition With Transfer Learning","display_name":"SeqNet: Sequential Networks for One-Shot Traffic Sign Recognition With Transfer Learning","publication_year":2024,"publication_date":"2024-09-23","ids":{"openalex":"https://openalex.org/W4402742457","doi":"https://doi.org/10.1109/tits.2024.3457583"},"language":"en","primary_location":{"id":"doi:10.1109/tits.2024.3457583","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tits.2024.3457583","pdf_url":null,"source":{"id":"https://openalex.org/S144771191","display_name":"IEEE Transactions on Intelligent Transportation Systems","issn_l":"1524-9050","issn":["1524-9050","1558-0016"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Intelligent Transportation Systems","raw_type":"journal-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/A5092163327","display_name":"Nariman Abdi","orcid":"https://orcid.org/0000-0003-2722-1770"},"institutions":[{"id":"https://openalex.org/I39268498","display_name":"University of Isfahan","ror":"https://ror.org/05h9t7759","country_code":"IR","type":"education","lineage":["https://openalex.org/I39268498"]}],"countries":["IR"],"is_corresponding":true,"raw_author_name":"Nariman Abdi","raw_affiliation_strings":["Department of Electrical Engineering, University of Isfahan, Isfahan, Iran"],"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, University of Isfahan, Isfahan, Iran","institution_ids":["https://openalex.org/I39268498"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006066917","display_name":"Farzad Parvaresh","orcid":"https://orcid.org/0000-0001-5292-0915"},"institutions":[{"id":"https://openalex.org/I39268498","display_name":"University of Isfahan","ror":"https://ror.org/05h9t7759","country_code":"IR","type":"education","lineage":["https://openalex.org/I39268498"]}],"countries":["IR"],"is_corresponding":false,"raw_author_name":"Farzad Parvaresh","raw_affiliation_strings":["Department of Electrical Engineering, University of Isfahan, Isfahan, Iran"],"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, University of Isfahan, Isfahan, Iran","institution_ids":["https://openalex.org/I39268498"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5054452842","display_name":"Mohamad Farzan Sabahi","orcid":"https://orcid.org/0000-0003-2359-2582"},"institutions":[{"id":"https://openalex.org/I39268498","display_name":"University of Isfahan","ror":"https://ror.org/05h9t7759","country_code":"IR","type":"education","lineage":["https://openalex.org/I39268498"]}],"countries":["IR"],"is_corresponding":false,"raw_author_name":"Mohamad Farzan Sabahi","raw_affiliation_strings":["Department of Electrical Engineering, University of Isfahan, Isfahan, Iran"],"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, University of Isfahan, Isfahan, Iran","institution_ids":["https://openalex.org/I39268498"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5092163327"],"corresponding_institution_ids":["https://openalex.org/I39268498"],"apc_list":null,"apc_paid":null,"fwci":2.1822,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.8944802,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":"25","issue":"12","first_page":"20435","last_page":"20445"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9771000146865845,"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"}},"topics":[{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9771000146865845,"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/T12707","display_name":"Vehicle License Plate Recognition","score":0.9728000164031982,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9692000150680542,"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":0.612524688243866},{"id":"https://openalex.org/keywords/transfer-of-learning","display_name":"Transfer of learning","score":0.5839681625366211},{"id":"https://openalex.org/keywords/traffic-sign-recognition","display_name":"Traffic sign recognition","score":0.5622959733009338},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5295876860618591},{"id":"https://openalex.org/keywords/shot","display_name":"Shot (pellet)","score":0.4582774341106415},{"id":"https://openalex.org/keywords/one-shot","display_name":"One shot","score":0.4143880009651184},{"id":"https://openalex.org/keywords/sign","display_name":"Sign (mathematics)","score":0.4100354015827179},{"id":"https://openalex.org/keywords/traffic-sign","display_name":"Traffic sign","score":0.3709180951118469},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.36222708225250244},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.34845978021621704},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.18614143133163452},{"id":"https://openalex.org/keywords/chemistry","display_name":"Chemistry","score":0.11058443784713745},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.08880776166915894}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.612524688243866},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.5839681625366211},{"id":"https://openalex.org/C6528762","wikidata":"https://www.wikidata.org/wiki/Q1574298","display_name":"Traffic sign recognition","level":4,"score":0.5622959733009338},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5295876860618591},{"id":"https://openalex.org/C2778344882","wikidata":"https://www.wikidata.org/wiki/Q278938","display_name":"Shot (pellet)","level":2,"score":0.4582774341106415},{"id":"https://openalex.org/C2992734406","wikidata":"https://www.wikidata.org/wiki/Q413267","display_name":"One shot","level":2,"score":0.4143880009651184},{"id":"https://openalex.org/C139676723","wikidata":"https://www.wikidata.org/wiki/Q1193832","display_name":"Sign (mathematics)","level":2,"score":0.4100354015827179},{"id":"https://openalex.org/C2983860417","wikidata":"https://www.wikidata.org/wiki/Q170285","display_name":"Traffic sign","level":3,"score":0.3709180951118469},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.36222708225250244},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.34845978021621704},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.18614143133163452},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.11058443784713745},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.08880776166915894},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C178790620","wikidata":"https://www.wikidata.org/wiki/Q11351","display_name":"Organic chemistry","level":1,"score":0.0},{"id":"https://openalex.org/C78519656","wikidata":"https://www.wikidata.org/wiki/Q101333","display_name":"Mechanical engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tits.2024.3457583","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tits.2024.3457583","pdf_url":null,"source":{"id":"https://openalex.org/S144771191","display_name":"IEEE Transactions on Intelligent Transportation Systems","issn_l":"1524-9050","issn":["1524-9050","1558-0016"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Intelligent Transportation Systems","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":42,"referenced_works":["https://openalex.org/W1980972548","https://openalex.org/W2031039904","https://openalex.org/W2108598243","https://openalex.org/W2117876524","https://openalex.org/W2167366427","https://openalex.org/W2194775991","https://openalex.org/W2479866714","https://openalex.org/W2773584821","https://openalex.org/W2792806930","https://openalex.org/W2963163009","https://openalex.org/W2963331658","https://openalex.org/W2963446712","https://openalex.org/W2963564750","https://openalex.org/W2963690607","https://openalex.org/W2963943197","https://openalex.org/W2964105864","https://openalex.org/W2965555521","https://openalex.org/W2970752219","https://openalex.org/W3027585699","https://openalex.org/W3041133507","https://openalex.org/W3090391847","https://openalex.org/W3094111018","https://openalex.org/W3120984755","https://openalex.org/W3205249428","https://openalex.org/W4200350681","https://openalex.org/W4213076383","https://openalex.org/W4223994295","https://openalex.org/W4293428643","https://openalex.org/W4309956283","https://openalex.org/W4313855698","https://openalex.org/W6618372016","https://openalex.org/W6677604277","https://openalex.org/W6680300913","https://openalex.org/W6682132143","https://openalex.org/W6717697761","https://openalex.org/W6741194474","https://openalex.org/W6768230505","https://openalex.org/W6770404601","https://openalex.org/W6783596713","https://openalex.org/W6790109781","https://openalex.org/W6790554731","https://openalex.org/W6810261015"],"related_works":["https://openalex.org/W4382897155","https://openalex.org/W4283820116","https://openalex.org/W4379231512","https://openalex.org/W4378699879","https://openalex.org/W3128164723","https://openalex.org/W4286647459","https://openalex.org/W2899819381","https://openalex.org/W2557202782","https://openalex.org/W3215426395","https://openalex.org/W2497720472"],"abstract_inverted_index":{"In":[0],"traffic":[1,112],"sign":[2,113],"recognition":[3,5,28,114],"tasks,":[4],"of":[6,66,101,166,190],"road":[7],"signs":[8],"by":[9,22,119,220],"observing":[10],"synthetic":[11],"reference":[12],"images":[13],"is":[14,29,55,77,218],"a":[15,30,39,56,67,87,170],"human-like":[16],"ability":[17],"that":[18,156,213],"can":[19],"be":[20,197],"performed":[21],"one-shot":[23,111,116,229],"learning":[24],"algorithms.":[25],"One-shot":[26],"object":[27],"challenging":[31],"task":[32,80],"for":[33,228,238],"deep":[34,40,68,89,172],"neural":[35],"networks":[36],"in":[37,81,110,158,199,202],"which":[38],"model":[41,69,125,132,159,173],"classifies":[42],"query":[43,62],"examples":[44],"based":[45],"on":[46,70,134],"support":[47,60],"images.":[48],"It":[49],"becomes":[50,176],"more":[51],"difficult":[52],"when":[53],"there":[54],"domain":[57,73,150],"shift":[58],"between":[59],"and":[61,115,178,231],"samples.":[63],"The":[64,187,209],"generalization":[65],"an":[71,146],"unknown":[72],"with":[74,223],"different":[75],"distributions":[76],"another":[78],"problematic":[79],"on-shot":[82],"recognition.":[83],"This":[84],"work":[85,105],"introduces":[86],"novel":[88],"network":[90,154],"named":[91],"SeqNet":[92,124,175,192,216],"to":[93,127,196,225],"overcome":[94],"the":[95,99,135,153,164,174,191,235],"aforementioned":[96],"problems.":[97],"To":[98],"best":[100],"our":[102,214],"knowledge,":[103],"this":[104],"outperforms":[106],"all":[107],"state-of-the-art":[108],"models":[109],"logo":[117],"identification":[118],"superior":[120],"results.":[121],"Our":[122],"proposed":[123,215],"generalizes":[126],"unseen":[128],"domains":[129],"without":[130],"further":[131],"fine-tuning":[133],"test":[136],"data.":[137],"Also,":[138],"we":[139],"show":[140],"how":[141],"using":[142],"transferred":[143,167],"knowledge":[144,168],"from":[145,169],"irrelevant":[147],"but":[148],"large":[149,171,221],"could":[151],"reduce":[152],"parameters":[155,183],"result":[157],"size":[160,189],"reduction.":[161],"By":[162],"utilizing":[163],"power":[165],"smaller":[177,188],"has":[179],"about":[180],"6X":[181],"fewer":[182],"than":[184],"its":[185],"competitors.":[186],"architecture":[193],"enables":[194],"it":[195],"used":[198],"resource-constrained":[200],"devices":[201],"many":[203],"applications":[204],"such":[205],"as":[206],"smart":[207],"vehicles.":[208],"experimental":[210],"results":[211],"depict":[212],"performance":[217],"ameliorated":[219],"margins,":[222],"up":[224],"20%":[226],"accuracy":[227],"classification":[230],"30%":[232],"area":[233],"under":[234],"curve":[236],"(AUC)":[237],"image":[239],"retrieval":[240],"tasks.":[241]},"counts_by_year":[{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":1}],"updated_date":"2025-12-21T01:58:51.020947","created_date":"2025-10-10T00:00:00"}