{"id":"https://openalex.org/W3092987574","doi":"https://doi.org/10.1145/3394171.3413561","title":"F2GAN: Fusing-and-Filling GAN for Few-shot Image Generation","display_name":"F2GAN: Fusing-and-Filling GAN for Few-shot Image Generation","publication_year":2020,"publication_date":"2020-10-12","ids":{"openalex":"https://openalex.org/W3092987574","doi":"https://doi.org/10.1145/3394171.3413561","mag":"3092987574"},"language":"en","primary_location":{"id":"doi:10.1145/3394171.3413561","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3394171.3413561","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 28th ACM International Conference on Multimedia","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/A5107194390","display_name":"Yan Hong","orcid":"https://orcid.org/0000-0001-6401-0812"},"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":"Yan Hong","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":"middle","author":{"id":"https://openalex.org/A5032618817","display_name":"Li Niu","orcid":"https://orcid.org/0000-0003-1970-8634"},"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":"Li Niu","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":"middle","author":{"id":"https://openalex.org/A5100395004","display_name":"Jianfu Zhang","orcid":"https://orcid.org/0000-0002-2673-5860"},"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":"Jianfu Zhang","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":"middle","author":{"id":"https://openalex.org/A5011623954","display_name":"Weijie Zhao","orcid":"https://orcid.org/0000-0002-8370-8308"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Weijie Zhao","raw_affiliation_strings":["Versa-AI, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"Versa-AI, Shanghai, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5039118363","display_name":"Chen Fu","orcid":"https://orcid.org/0000-0001-6554-1770"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen Fu","raw_affiliation_strings":["Versa-AI, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"Versa-AI, Shanghai, China","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100741568","display_name":"Liqing Zhang","orcid":"https://orcid.org/0000-0001-7597-8503"},"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":"Liqing Zhang","raw_affiliation_strings":["Shanghai Jiao Tong Univercity, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"Shanghai Jiao Tong Univercity, Shanghai, China","institution_ids":["https://openalex.org/I183067930"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5107194390"],"corresponding_institution_ids":["https://openalex.org/I183067930"],"apc_list":null,"apc_paid":null,"fwci":5.3734,"has_fulltext":false,"cited_by_count":87,"citation_normalized_percentile":{"value":0.96729851,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"2535","last_page":"2543"},"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":0.9995999932289124,"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":0.9995999932289124,"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":0.9983999729156494,"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/T13114","display_name":"Image Processing Techniques and Applications","score":0.9868000149726868,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/discriminator","display_name":"Discriminator","score":0.7800503373146057},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7473203539848328},{"id":"https://openalex.org/keywords/fuse","display_name":"Fuse (electrical)","score":0.7376759052276611},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7231447100639343},{"id":"https://openalex.org/keywords/interpolation","display_name":"Interpolation (computer graphics)","score":0.5968958139419556},{"id":"https://openalex.org/keywords/generator","display_name":"Generator (circuit theory)","score":0.5900730490684509},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.5696417689323425},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5101409554481506},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4266047775745392}],"concepts":[{"id":"https://openalex.org/C2779803651","wikidata":"https://www.wikidata.org/wiki/Q5282088","display_name":"Discriminator","level":3,"score":0.7800503373146057},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7473203539848328},{"id":"https://openalex.org/C141353440","wikidata":"https://www.wikidata.org/wiki/Q182221","display_name":"Fuse (electrical)","level":2,"score":0.7376759052276611},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7231447100639343},{"id":"https://openalex.org/C137800194","wikidata":"https://www.wikidata.org/wiki/Q11713455","display_name":"Interpolation (computer graphics)","level":3,"score":0.5968958139419556},{"id":"https://openalex.org/C2780992000","wikidata":"https://www.wikidata.org/wiki/Q17016113","display_name":"Generator (circuit theory)","level":3,"score":0.5900730490684509},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.5696417689323425},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5101409554481506},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4266047775745392},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.0},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3394171.3413561","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3394171.3413561","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 28th ACM International Conference on Multimedia","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","score":0.7300000190734863,"id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":44,"referenced_works":["https://openalex.org/W1959608418","https://openalex.org/W2114168642","https://openalex.org/W2163605009","https://openalex.org/W2183341477","https://openalex.org/W2194775991","https://openalex.org/W2401823607","https://openalex.org/W2533598788","https://openalex.org/W2548275288","https://openalex.org/W2550553598","https://openalex.org/W2590796488","https://openalex.org/W2605287558","https://openalex.org/W2765407302","https://openalex.org/W2770173563","https://openalex.org/W2786015036","https://openalex.org/W2799251726","https://openalex.org/W2804078698","https://openalex.org/W2896446917","https://openalex.org/W2896736448","https://openalex.org/W2942626212","https://openalex.org/W2949247522","https://openalex.org/W2951326654","https://openalex.org/W2951775809","https://openalex.org/W2953030256","https://openalex.org/W2962770929","https://openalex.org/W2962785568","https://openalex.org/W2963091558","https://openalex.org/W2963306805","https://openalex.org/W2963341924","https://openalex.org/W2963656855","https://openalex.org/W2963767194","https://openalex.org/W2963839617","https://openalex.org/W2963981733","https://openalex.org/W2964105864","https://openalex.org/W2964249870","https://openalex.org/W2964660724","https://openalex.org/W2979689312","https://openalex.org/W2981812367","https://openalex.org/W2991997773","https://openalex.org/W2992308087","https://openalex.org/W2996623013","https://openalex.org/W3009297661","https://openalex.org/W3034296706","https://openalex.org/W3034684802","https://openalex.org/W3035574324"],"related_works":["https://openalex.org/W2953246223","https://openalex.org/W4293320219","https://openalex.org/W3110074278","https://openalex.org/W4283584549","https://openalex.org/W2618858825","https://openalex.org/W2554314924","https://openalex.org/W2998859928","https://openalex.org/W4381885966","https://openalex.org/W4288256692","https://openalex.org/W4308217387"],"abstract_inverted_index":{"In":[0,73,99],"order":[1],"to":[2,48,56,84,107,130],"generate":[3,57,85],"images":[4,58,63,89,114,144],"for":[5,45,64,90,167],"a":[6,49,61,65,78,91,96,102,132,146],"given":[7],"category,":[8,67],"existing":[9,40],"deep":[10],"generative":[11],"models":[12],"generally":[13],"rely":[14],"on":[15,157],"abundant":[16],"training":[17],"images.":[18,98],"However,":[19],"extensive":[20],"data":[21,31],"acquisition":[22],"is":[23,32,105],"expensive":[24],"and":[25,87,119,150],"fast":[26,46],"learning":[27],"ability":[28],"from":[29,59],"limited":[30],"necessarily":[33],"required":[34],"in":[35,122],"real-world":[36],"applications.":[37],"Also,":[38],"these":[39],"methods":[41],"are":[42],"not":[43],"well-suited":[44],"adaptation":[47],"new":[50,66,92,133],"category.":[51],"Few-shot":[52],"image":[53,169],"generation,":[54],"aiming":[55],"only":[60,95],"few":[62,97],"has":[68],"attracted":[69],"some":[70],"research":[71],"interest.":[72],"this":[74],"paper,":[75],"we":[76],"propose":[77],"Fusing-and-Filling":[79],"Generative":[80],"Adversarial":[81],"Network":[82],"(F2GAN)":[83],"realistic":[86],"diverse":[88],"category":[93],"with":[94,115,126],"our":[100,136,164],"F2GAN,":[101],"fusion":[103],"generator":[104],"designed":[106],"fuse":[108],"the":[109,140,161],"high-level":[110],"features":[111],"of":[112,142,163],"conditional":[113],"random":[116],"interpolation":[117,152],"coefficients,":[118],"then":[120],"fills":[121],"attended":[123],"low-level":[124],"details":[125],"non-local":[127],"attention":[128],"module":[129],"produce":[131],"image.":[134],"Moreover,":[135],"discriminator":[137],"can":[138],"ensure":[139],"diversity":[141],"generated":[143],"by":[145],"mode":[147],"seeking":[148],"loss":[149],"an":[151],"regression":[153],"loss.":[154],"Extensive":[155],"experiments":[156],"five":[158],"datasets":[159],"demonstrate":[160],"effectiveness":[162],"proposed":[165],"method":[166],"few-shot":[168],"generation.":[170]},"counts_by_year":[{"year":2025,"cited_by_count":13},{"year":2024,"cited_by_count":19},{"year":2023,"cited_by_count":28},{"year":2022,"cited_by_count":20},{"year":2021,"cited_by_count":7}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
