{"id":"https://openalex.org/W4409536958","doi":"https://doi.org/10.1109/icvisp64524.2024.10959589","title":"Enhancing VQGAN Performance Through Integration of Multiple Vision Transformers","display_name":"Enhancing VQGAN Performance Through Integration of Multiple Vision Transformers","publication_year":2024,"publication_date":"2024-12-27","ids":{"openalex":"https://openalex.org/W4409536958","doi":"https://doi.org/10.1109/icvisp64524.2024.10959589"},"language":"en","primary_location":{"id":"doi:10.1109/icvisp64524.2024.10959589","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icvisp64524.2024.10959589","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE 8th International Conference on Vision, Image and Signal Processing (ICVISP)","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":null,"display_name":"Tianyu Chen","orcid":"https://orcid.org/0009-0000-6021-763X"},"institutions":[{"id":"https://openalex.org/I50760025","display_name":"Hangzhou Dianzi University","ror":"https://ror.org/0576gt767","country_code":"CN","type":"education","lineage":["https://openalex.org/I50760025"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Tianyu Chen","raw_affiliation_strings":["College of computer science and technology, Hangzhou Dianzi University,Hangzhou,China"],"raw_orcid":"https://orcid.org/0009-0000-6021-763X","affiliations":[{"raw_affiliation_string":"College of computer science and technology, Hangzhou Dianzi University,Hangzhou,China","institution_ids":["https://openalex.org/I50760025"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058073009","display_name":"Daxing Zhang","orcid":"https://orcid.org/0000-0003-1746-1666"},"institutions":[{"id":"https://openalex.org/I50760025","display_name":"Hangzhou Dianzi University","ror":"https://ror.org/0576gt767","country_code":"CN","type":"education","lineage":["https://openalex.org/I50760025"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Daxing Zhang","raw_affiliation_strings":["College of computer science and technology, Hangzhou Dianzi University,Hangzhou,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of computer science and technology, Hangzhou Dianzi University,Hangzhou,China","institution_ids":["https://openalex.org/I50760025"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5007342270","display_name":"Yihao Qian","orcid":null},"institutions":[{"id":"https://openalex.org/I50760025","display_name":"Hangzhou Dianzi University","ror":"https://ror.org/0576gt767","country_code":"CN","type":"education","lineage":["https://openalex.org/I50760025"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yihao Qian","raw_affiliation_strings":["College of computer science and technology, Hangzhou Dianzi University,Hangzhou,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of computer science and technology, Hangzhou Dianzi University,Hangzhou,China","institution_ids":["https://openalex.org/I50760025"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I50760025"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.26663921,"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/T11992","display_name":"CCD and CMOS Imaging Sensors","score":0.9927999973297119,"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/T11992","display_name":"CCD and CMOS Imaging Sensors","score":0.9927999973297119,"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/T12389","display_name":"Infrared Target Detection Methodologies","score":0.9516000151634216,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6140828132629395},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.46284693479537964},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.43650734424591064},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.35686397552490234},{"id":"https://openalex.org/keywords/electrical-engineering","display_name":"Electrical engineering","score":0.1978435516357422},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.18117240071296692},{"id":"https://openalex.org/keywords/voltage","display_name":"Voltage","score":0.11306518316268921}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6140828132629395},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.46284693479537964},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.43650734424591064},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.35686397552490234},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.1978435516357422},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.18117240071296692},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.11306518316268921}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icvisp64524.2024.10959589","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icvisp64524.2024.10959589","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE 8th International Conference on Vision, Image and Signal Processing (ICVISP)","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":49,"referenced_works":["https://openalex.org/W2108598243","https://openalex.org/W2136023023","https://openalex.org/W2150593711","https://openalex.org/W2194775991","https://openalex.org/W2533598788","https://openalex.org/W2563096758","https://openalex.org/W2752796333","https://openalex.org/W2962770929","https://openalex.org/W2963446712","https://openalex.org/W2971193605","https://openalex.org/W2980775160","https://openalex.org/W3009270141","https://openalex.org/W3019913914","https://openalex.org/W3048804154","https://openalex.org/W3094502228","https://openalex.org/W3126750203","https://openalex.org/W3138516171","https://openalex.org/W3151130473","https://openalex.org/W3172329356","https://openalex.org/W3176179930","https://openalex.org/W3180355996","https://openalex.org/W4214493665","https://openalex.org/W4246193833","https://openalex.org/W4296917279","https://openalex.org/W4315705623","https://openalex.org/W4385245566","https://openalex.org/W4386076522","https://openalex.org/W4388202396","https://openalex.org/W4388893580","https://openalex.org/W4389892040","https://openalex.org/W4390939169","https://openalex.org/W4392305765","https://openalex.org/W4398785195","https://openalex.org/W4400721967","https://openalex.org/W6637373629","https://openalex.org/W6685352114","https://openalex.org/W6686980507","https://openalex.org/W6720208624","https://openalex.org/W6736210646","https://openalex.org/W6741832134","https://openalex.org/W6748582592","https://openalex.org/W6755312952","https://openalex.org/W6787575185","https://openalex.org/W6796761347","https://openalex.org/W6797572590","https://openalex.org/W6800217721","https://openalex.org/W6810023147","https://openalex.org/W6842542540","https://openalex.org/W6845452229"],"related_works":["https://openalex.org/W2772917594","https://openalex.org/W2036807459","https://openalex.org/W2058170566","https://openalex.org/W2755342338","https://openalex.org/W2166024367","https://openalex.org/W3116076068","https://openalex.org/W2229312674","https://openalex.org/W2951359407","https://openalex.org/W2079911747","https://openalex.org/W1969923398"],"abstract_inverted_index":{"This":[0,96],"paper":[1],"addresses":[2],"the":[3,7,52,104,111],"problem":[4],"of":[5,11,56,61,113],"improving":[6],"initialisation":[8],"and":[9,54,73,78,102,115],"performance":[10],"Vector":[12],"Quantised":[13],"Generative":[14],"Adversarial":[15],"Networks":[16],"(VQGANs).":[17],"Our":[18],"contribution":[19],"lies":[20],"in":[21,84],"integrating":[22],"features":[23,38],"from":[24],"multiple":[25],"Vision":[26],"Transformer":[27],"(ViT)":[28],"models":[29],"to":[30,43],"create":[31],"a":[32],"robust":[33],"feature":[34],"extraction":[35],"framework.":[36],"These":[37],"are":[39],"clustered":[40],"using":[41],"k-means":[42],"form":[44],"an":[45],"enriched":[46],"codebook":[47],"for":[48,106],"VQGAN,":[49],"which":[50],"enhances":[51],"fidelity":[53],"stability":[55],"generated":[57],"images.":[58],"The":[59],"results":[60],"our":[62],"experiments,":[63],"conducted":[64],"on":[65],"datasets":[66],"such":[67],"as":[68,87],"Oxford":[69],"Flowers":[70],"102,":[71],"Oxford-IIIT-Pet,":[72],"specific":[74],"ImageNet-1K":[75],"categories":[76],"(birds":[77],"architectural":[79],"structures),":[80],"demonstrate":[81],"significant":[82],"improvements":[83],"image":[85,108],"quality,":[86],"evidenced":[88],"by":[89],"lower":[90],"Fr\u00e9chet":[91],"Inception":[92],"Distance":[93],"(FID)":[94],"scores.":[95],"methodology":[97],"advances":[98],"generative":[99],"modelling":[100],"techniques":[101],"highlights":[103],"potential":[105],"enhanced":[107],"synthesis":[109],"through":[110],"fusion":[112],"ViT":[114],"VQGAN":[116],"models.":[117]},"counts_by_year":[],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}