{"id":"https://openalex.org/W3197347859","doi":"https://doi.org/10.1145/3460426.3463651","title":"Generative Adversarial Networks with Bi-directional Normalization for Semantic Image Synthesis","display_name":"Generative Adversarial Networks with Bi-directional Normalization for Semantic Image Synthesis","publication_year":2021,"publication_date":"2021-08-24","ids":{"openalex":"https://openalex.org/W3197347859","doi":"https://doi.org/10.1145/3460426.3463651","mag":"3197347859"},"language":"en","primary_location":{"id":"doi:10.1145/3460426.3463651","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3460426.3463651","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2021 International Conference on Multimedia Retrieval","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/A5083563662","display_name":"Jia Long","orcid":null},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jia Long","raw_affiliation_strings":["Shanghai Jiao Tong University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"Shanghai Jiao Tong University, Shanghai, China","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5102899381","display_name":"Hongtao Lu","orcid":"https://orcid.org/0000-0003-2300-3039"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hongtao Lu","raw_affiliation_strings":["Shanghai Jiao Tong University, Shanghai , China"],"affiliations":[{"raw_affiliation_string":"Shanghai Jiao Tong University, Shanghai , China","institution_ids":["https://openalex.org/I183067930"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5083563662"],"corresponding_institution_ids":["https://openalex.org/I183067930"],"apc_list":null,"apc_paid":null,"fwci":0.1921,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.48968954,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"99","issue":null,"first_page":"219","last_page":"226"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.9994000196456909,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.9994000196456909,"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/T10531","display_name":"Advanced Vision and Imaging","score":0.996999979019165,"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/T11105","display_name":"Advanced Image Processing Techniques","score":0.9959999918937683,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8286757469177246},{"id":"https://openalex.org/keywords/normalization","display_name":"Normalization (sociology)","score":0.7306053638458252},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.602234959602356},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.5319564342498779},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.45138224959373474},{"id":"https://openalex.org/keywords/semantic-feature","display_name":"Semantic feature","score":0.4328959882259369},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3471084237098694}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8286757469177246},{"id":"https://openalex.org/C136886441","wikidata":"https://www.wikidata.org/wiki/Q926129","display_name":"Normalization (sociology)","level":2,"score":0.7306053638458252},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.602234959602356},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.5319564342498779},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.45138224959373474},{"id":"https://openalex.org/C2781122975","wikidata":"https://www.wikidata.org/wiki/Q16928266","display_name":"Semantic feature","level":2,"score":0.4328959882259369},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3471084237098694},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0},{"id":"https://openalex.org/C19165224","wikidata":"https://www.wikidata.org/wiki/Q23404","display_name":"Anthropology","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3460426.3463651","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3460426.3463651","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2021 International Conference on Multimedia Retrieval","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","score":0.49000000953674316,"id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W1836465849","https://openalex.org/W2097117768","https://openalex.org/W2340897893","https://openalex.org/W2561196672","https://openalex.org/W2603777577","https://openalex.org/W2737258237","https://openalex.org/W2785678896","https://openalex.org/W2890816492","https://openalex.org/W2895749211","https://openalex.org/W2904367110","https://openalex.org/W2950893734","https://openalex.org/W2952122856","https://openalex.org/W2952716587","https://openalex.org/W2961363059","https://openalex.org/W2962770929","https://openalex.org/W2962960082","https://openalex.org/W2962982136","https://openalex.org/W2963522749","https://openalex.org/W2963800363","https://openalex.org/W2963966654","https://openalex.org/W2963981733","https://openalex.org/W3048484056","https://openalex.org/W3122887115","https://openalex.org/W3124902815"],"related_works":["https://openalex.org/W2953716828","https://openalex.org/W2904857019","https://openalex.org/W2944728705","https://openalex.org/W3011538607","https://openalex.org/W2904022177","https://openalex.org/W2591697403","https://openalex.org/W4321441197","https://openalex.org/W2359348847","https://openalex.org/W4311555960","https://openalex.org/W4390871823"],"abstract_inverted_index":{"Semantic":[0],"image":[1,132,156],"synthesis":[2],"aims":[3],"at":[4],"translating":[5],"semantic":[6,45,68,103,116,120,125,131,150],"label":[7,46,151],"maps":[8],"to":[9,49,56,80,88,112,122,144,159],"photo-realistic":[10],"images.":[11,171],"However,":[12],"most":[13],"of":[14,26,130,184],"previous":[15],"methods":[16,41,186],"easily":[17],"generate":[18],"blurred":[19],"regions":[20],"and":[21,23,62,92,105,118,153,191],"artifacts,":[22],"the":[24,44,50,58,67,102,106,115,124,128],"quality":[25,170],"these":[27,40,146],"images":[28],"is":[29],"far":[30],"from":[31,72],"realistic.":[32],"There":[33],"are":[34,70,77,96],"two":[35],"unresolved":[36],"problems":[37],"existing:":[38],"first,":[39],"directly":[42],"feed":[43],"as":[47],"input":[48],"deep":[51],"network,":[52],"through":[53],"convolution":[54],"operation":[55],"produce":[57],"normalization":[59],"parameters":[60],"\u03b3":[61],"\u03b2,":[63],"we":[64],"find":[65],"that":[66,182],"labels":[69,104],"different":[71],"real":[73,107,154],"scene":[74,108,155],"images,":[75,109],"they":[76],"not":[78],"able":[79],"provide":[81],"detailed":[82],"structural":[83],"information,":[84],"making":[85],"it":[86],"difficult":[87],"synthesize":[89],"local":[90],"details":[91],"structures;":[93],"second,":[94],"there":[95],"no":[97],"bi-directional":[98,165],"information":[99,117,126,152],"flow":[100],"between":[101],"this":[110],"leads":[111],"inefficiently":[113],"utilize":[114],"maintain":[119],"constrains":[121],"preserve":[123],"in":[127,139,187],"process":[129],"synthesis.":[133],"We":[134],"propose":[135],"Bi-directional":[136],"Normalization":[137],"(BDN)":[138],"our":[140],"generative":[141],"adversarial":[142],"networks":[143],"solve":[145],"problems,":[147],"which":[148],"allows":[149],"feature":[157],"representation":[158],"be":[160],"effectively":[161],"utilized":[162],"by":[163],"a":[164],"way":[166],"for":[167],"generating":[168],"high":[169],"Extensive":[172],"experiments":[173],"on":[174],"several":[175],"challenging":[176],"datasets":[177],"demonstrate":[178],"significantly":[179],"better":[180],"than":[181],"results":[183],"existing":[185],"both":[188],"visual":[189],"fidelity":[190],"quantitative":[192],"metrics.":[193]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
