{"id":"https://openalex.org/W4364297064","doi":"https://doi.org/10.1109/aipr57179.2022.10092222","title":"Generative Adversarial Networks for Vehicle Transformation in Overhead and Satellite Imagery","display_name":"Generative Adversarial Networks for Vehicle Transformation in Overhead and Satellite Imagery","publication_year":2022,"publication_date":"2022-10-11","ids":{"openalex":"https://openalex.org/W4364297064","doi":"https://doi.org/10.1109/aipr57179.2022.10092222"},"language":"en","primary_location":{"id":"doi:10.1109/aipr57179.2022.10092222","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/aipr57179.2022.10092222","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)","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/A5058472222","display_name":"Michael Reale","orcid":"https://orcid.org/0000-0003-2147-4443"},"institutions":[{"id":"https://openalex.org/I90965887","display_name":"SUNY Polytechnic Institute","ror":"https://ror.org/000fxgx19","country_code":"US","type":"education","lineage":["https://openalex.org/I90965887"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Michael J. Reale","raw_affiliation_strings":["SUNY Polytechnic,Computer &amp; Information Science Department,Utica,NY,USA","Computer & Information Science Department, SUNY Polytechnic, Utica, NY, USA"],"affiliations":[{"raw_affiliation_string":"SUNY Polytechnic,Computer &amp; Information Science Department,Utica,NY,USA","institution_ids":["https://openalex.org/I90965887"]},{"raw_affiliation_string":"Computer & Information Science Department, SUNY Polytechnic, Utica, NY, USA","institution_ids":["https://openalex.org/I90965887"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017559306","display_name":"Preston Nichols","orcid":null},"institutions":[{"id":"https://openalex.org/I90965887","display_name":"SUNY Polytechnic Institute","ror":"https://ror.org/000fxgx19","country_code":"US","type":"education","lineage":["https://openalex.org/I90965887"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Preston Nichols","raw_affiliation_strings":["SUNY Polytechnic,Computer &amp; Information Science Department,Utica,NY,USA","Computer & Information Science Department, SUNY Polytechnic, Utica, NY, USA"],"affiliations":[{"raw_affiliation_string":"SUNY Polytechnic,Computer &amp; Information Science Department,Utica,NY,USA","institution_ids":["https://openalex.org/I90965887"]},{"raw_affiliation_string":"Computer & Information Science Department, SUNY Polytechnic, Utica, NY, USA","institution_ids":["https://openalex.org/I90965887"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5002539051","display_name":"\u0395. A. Schneider","orcid":"https://orcid.org/0009-0006-6323-4666"},"institutions":[{"id":"https://openalex.org/I90965887","display_name":"SUNY Polytechnic Institute","ror":"https://ror.org/000fxgx19","country_code":"US","type":"education","lineage":["https://openalex.org/I90965887"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ethan Schneider","raw_affiliation_strings":["SUNY Polytechnic,Computer &amp; Information Science Department,Utica,NY,USA","Computer & Information Science Department, SUNY Polytechnic, Utica, NY, USA"],"affiliations":[{"raw_affiliation_string":"SUNY Polytechnic,Computer &amp; Information Science Department,Utica,NY,USA","institution_ids":["https://openalex.org/I90965887"]},{"raw_affiliation_string":"Computer & Information Science Department, SUNY Polytechnic, Utica, NY, USA","institution_ids":["https://openalex.org/I90965887"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5088334760","display_name":"Morgan Bishop","orcid":null},"institutions":[{"id":"https://openalex.org/I1280414376","display_name":"United States Air Force Research Laboratory","ror":"https://ror.org/02e2egq70","country_code":"US","type":"facility","lineage":["https://openalex.org/I1280414376","https://openalex.org/I1330347796","https://openalex.org/I4210102105","https://openalex.org/I4389425425"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Morgan Bishop","raw_affiliation_strings":["AFRL/RIEBB,Rome,NY,USA","AFRL/RIEBB, Rome, NY, USA"],"affiliations":[{"raw_affiliation_string":"AFRL/RIEBB,Rome,NY,USA","institution_ids":["https://openalex.org/I1280414376"]},{"raw_affiliation_string":"AFRL/RIEBB, Rome, NY, USA","institution_ids":["https://openalex.org/I1280414376"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5010341160","display_name":"Maria Cornacchia","orcid":null},"institutions":[{"id":"https://openalex.org/I1280414376","display_name":"United States Air Force Research Laboratory","ror":"https://ror.org/02e2egq70","country_code":"US","type":"facility","lineage":["https://openalex.org/I1280414376","https://openalex.org/I1330347796","https://openalex.org/I4210102105","https://openalex.org/I4389425425"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Maria Cornacchia","raw_affiliation_strings":["AFRL/RIEBB,Rome,NY,USA","AFRL/RIEBB, Rome, NY, USA"],"affiliations":[{"raw_affiliation_string":"AFRL/RIEBB,Rome,NY,USA","institution_ids":["https://openalex.org/I1280414376"]},{"raw_affiliation_string":"AFRL/RIEBB, Rome, NY, USA","institution_ids":["https://openalex.org/I1280414376"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5058472222"],"corresponding_institution_ids":["https://openalex.org/I90965887"],"apc_list":null,"apc_paid":null,"fwci":0.1007,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.42486815,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"13"},"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":0.9997000098228455,"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":0.9997000098228455,"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":0.9945999979972839,"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/T12357","display_name":"Digital Media Forensic Detection","score":0.9926000237464905,"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":0.8135112524032593},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.7981964349746704},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6849402785301208},{"id":"https://openalex.org/keywords/overhead","display_name":"Overhead (engineering)","score":0.5636873245239258},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5403982996940613},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.5047866106033325},{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.49303337931632996},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.46533647179603577},{"id":"https://openalex.org/keywords/transformation","display_name":"Transformation (genetics)","score":0.4484187364578247},{"id":"https://openalex.org/keywords/generative-adversarial-network","display_name":"Generative adversarial network","score":0.43345701694488525}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8135112524032593},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.7981964349746704},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6849402785301208},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.5636873245239258},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5403982996940613},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.5047866106033325},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.49303337931632996},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.46533647179603577},{"id":"https://openalex.org/C204241405","wikidata":"https://www.wikidata.org/wiki/Q461499","display_name":"Transformation (genetics)","level":3,"score":0.4484187364578247},{"id":"https://openalex.org/C2988773926","wikidata":"https://www.wikidata.org/wiki/Q25104379","display_name":"Generative adversarial network","level":3,"score":0.43345701694488525},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/aipr57179.2022.10092222","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/aipr57179.2022.10092222","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/8","display_name":"Decent work and economic growth","score":0.44999998807907104}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W1901129140","https://openalex.org/W2888832231","https://openalex.org/W2962770929","https://openalex.org/W2963767194","https://openalex.org/W3034600949","https://openalex.org/W3035574324","https://openalex.org/W3088020020","https://openalex.org/W3176134838","https://openalex.org/W3176913662","https://openalex.org/W4235417891","https://openalex.org/W4242753176","https://openalex.org/W6729966448","https://openalex.org/W6736210646","https://openalex.org/W6754479648","https://openalex.org/W6765779288","https://openalex.org/W6771990991","https://openalex.org/W6779093361","https://openalex.org/W6787839309"],"related_works":["https://openalex.org/W2502115930","https://openalex.org/W2888032422","https://openalex.org/W2996316059","https://openalex.org/W4385421777","https://openalex.org/W4377980832","https://openalex.org/W2897769091","https://openalex.org/W2845413374","https://openalex.org/W3005996785","https://openalex.org/W4297411772","https://openalex.org/W4235873501"],"abstract_inverted_index":{"The":[0],"ability":[1,37],"to":[2,12,38,51,66,76,89],"create":[3,39],"and":[4,19,56,73,112,138,158],"detect":[5],"synthetic":[6,17,40],"video":[7,41],"is":[8],"becoming":[9],"critically":[10],"important":[11],"scene":[13],"understanding.":[14],"Techniques":[15],"for":[16,144,151],"manipulation":[18],"augmentation":[20,44,124,141],"of":[21,45,69,79,109,121,132],"data":[22,123],"increases":[23],"diversity":[24],"within":[25],"available":[26],"datasets,":[27],"while":[28],"not":[29,113],"requiring":[30],"laborious":[31],"labeling":[32],"efforts.":[33],"That":[34],"is,":[35],"the":[36,105,110,119],"can":[42],"enable":[43],"small":[46],"realistic":[47],"datasets":[48,160],"on":[49,155],"which":[50],"further":[52],"train":[53],"Artificial":[54],"Intelligence":[55],"Machine":[57],"Learning":[58],"(AI/ML)":[59],"algorithms.":[60],"Thus,":[61],"it":[62],"may":[63],"be":[64],"desirable":[65],"convert":[67],"images":[68],"vehicles":[70,133],"in":[71,107],"satellite":[72],"overhead":[74],"imagery":[75],"other":[77],"varieties":[78],"vehicles.":[80],"In":[81,115],"this":[82,152],"work,":[83],"we":[84,117],"leverage":[85,98],"generative":[86],"adversarial":[87],"networks":[88],"transform":[90],"cars":[91],"into":[92],"trucks":[93],"(and":[94],"vice":[95],"versa).":[96],"We":[97],"an":[99,135],"attention-based":[100],"masking":[101],"approach":[102],"that":[103],"assists":[104],"network":[106,146],"transformation":[108],"object":[111],"background.":[114],"addition,":[116],"demonstrate":[118],"benefits":[120],"numerous":[122],"procedures,":[125],"including":[126],"presenting":[127],"a":[128],"new":[129],"artificial":[130,159],"dataset":[131],"from":[134],"aerial":[136],"perspective":[137],"introducing":[139],"novel":[140],"techniques":[142],"appropriate":[143],"our":[145],"architectures.":[147],"Experiments":[148],"are":[149],"conducted":[150],"unique":[153],"application":[154],"both":[156],"real":[157],"with":[161],"state-of-the-art":[162],"results.":[163]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2025-12-21T01:58:51.020947","created_date":"2025-10-10T00:00:00"}
