{"id":"https://openalex.org/W2902100912","doi":"https://doi.org/10.1007/s11263-018-1132-0","title":"Which is the Better Inpainted Image?Training Data Generation Without Any Manual Operations","display_name":"Which is the Better Inpainted Image?Training Data Generation Without Any Manual Operations","publication_year":2018,"publication_date":"2018-11-25","ids":{"openalex":"https://openalex.org/W2902100912","doi":"https://doi.org/10.1007/s11263-018-1132-0","mag":"2902100912"},"language":"en","primary_location":{"id":"doi:10.1007/s11263-018-1132-0","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s11263-018-1132-0","pdf_url":"https://link.springer.com/content/pdf/10.1007/s11263-018-1132-0.pdf","source":{"id":"https://openalex.org/S25538012","display_name":"International Journal of Computer Vision","issn_l":"0920-5691","issn":["0920-5691","1573-1405"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Computer Vision","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://link.springer.com/content/pdf/10.1007/s11263-018-1132-0.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5029572039","display_name":"Mariko Isogawa","orcid":"https://orcid.org/0000-0001-9560-0276"},"institutions":[{"id":"https://openalex.org/I2251713219","display_name":"NTT (Japan)","ror":"https://ror.org/00berct97","country_code":"JP","type":"company","lineage":["https://openalex.org/I2251713219"]},{"id":"https://openalex.org/I98285908","display_name":"The University of Osaka","ror":"https://ror.org/035t8zc32","country_code":"JP","type":"education","lineage":["https://openalex.org/I98285908"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Mariko Isogawa","raw_affiliation_strings":["Graduate School of Engineering Science, Osaka University, Toyonaka, Japan","NTT Media Intelligence Laboratories, Yokosuka, Japan"],"raw_orcid":"https://orcid.org/0000-0001-9560-0276","affiliations":[{"raw_affiliation_string":"Graduate School of Engineering Science, Osaka University, Toyonaka, Japan","institution_ids":["https://openalex.org/I98285908"]},{"raw_affiliation_string":"NTT Media Intelligence Laboratories, Yokosuka, Japan","institution_ids":["https://openalex.org/I2251713219"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5056398983","display_name":"Dan Mikami","orcid":"https://orcid.org/0000-0002-6738-4761"},"institutions":[{"id":"https://openalex.org/I2251713219","display_name":"NTT (Japan)","ror":"https://ror.org/00berct97","country_code":"JP","type":"company","lineage":["https://openalex.org/I2251713219"]},{"id":"https://openalex.org/I4210105847","display_name":"NTT Basic Research Laboratories","ror":"https://ror.org/01m2pas06","country_code":"JP","type":"facility","lineage":["https://openalex.org/I4210105847"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Dan Mikami","raw_affiliation_strings":["NTT Communication Science Laboratories, Atsugi, Japan","NTT Media Intelligence Laboratories, Yokosuka, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"NTT Communication Science Laboratories, Atsugi, Japan","institution_ids":["https://openalex.org/I4210105847"]},{"raw_affiliation_string":"NTT Media Intelligence Laboratories, Yokosuka, Japan","institution_ids":["https://openalex.org/I2251713219"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5038138281","display_name":"Kosuke Takahashi","orcid":"https://orcid.org/0000-0003-2361-5043"},"institutions":[{"id":"https://openalex.org/I2251713219","display_name":"NTT (Japan)","ror":"https://ror.org/00berct97","country_code":"JP","type":"company","lineage":["https://openalex.org/I2251713219"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Kosuke Takahashi","raw_affiliation_strings":["NTT Media Intelligence Laboratories, Yokosuka, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"NTT Media Intelligence Laboratories, Yokosuka, Japan","institution_ids":["https://openalex.org/I2251713219"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5053408297","display_name":"Daisuke Iwai","orcid":"https://orcid.org/0000-0002-3493-5635"},"institutions":[{"id":"https://openalex.org/I98285908","display_name":"The University of Osaka","ror":"https://ror.org/035t8zc32","country_code":"JP","type":"education","lineage":["https://openalex.org/I98285908"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Daisuke Iwai","raw_affiliation_strings":["Graduate School of Engineering Science, Osaka University, Toyonaka, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Graduate School of Engineering Science, Osaka University, Toyonaka, Japan","institution_ids":["https://openalex.org/I98285908"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5048671334","display_name":"Kosuke Sato","orcid":"https://orcid.org/0000-0003-1429-9990"},"institutions":[{"id":"https://openalex.org/I98285908","display_name":"The University of Osaka","ror":"https://ror.org/035t8zc32","country_code":"JP","type":"education","lineage":["https://openalex.org/I98285908"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Kosuke Sato","raw_affiliation_strings":["Graduate School of Engineering Science, Osaka University, Toyonaka, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Graduate School of Engineering Science, Osaka University, Toyonaka, Japan","institution_ids":["https://openalex.org/I98285908"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5079940782","display_name":"Hideaki Kimata","orcid":"https://orcid.org/0000-0003-1287-6862"},"institutions":[{"id":"https://openalex.org/I2251713219","display_name":"NTT (Japan)","ror":"https://ror.org/00berct97","country_code":"JP","type":"company","lineage":["https://openalex.org/I2251713219"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Hideaki Kimata","raw_affiliation_strings":["NTT Media Intelligence Laboratories, Yokosuka, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"NTT Media Intelligence Laboratories, Yokosuka, Japan","institution_ids":["https://openalex.org/I2251713219"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5029572039"],"corresponding_institution_ids":["https://openalex.org/I2251713219","https://openalex.org/I98285908"],"apc_list":{"value":2890,"currency":"EUR","value_usd":3690},"apc_paid":{"value":2890,"currency":"EUR","value_usd":3690},"fwci":0.5311,"has_fulltext":true,"cited_by_count":7,"citation_normalized_percentile":{"value":0.72291566,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":"127","issue":"11-12","first_page":"1751","last_page":"1766"},"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":0.9965000152587891,"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":0.9965000152587891,"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":0.992900013923645,"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/T11165","display_name":"Image and Video Quality Assessment","score":0.9873999953269958,"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/inpainting","display_name":"Inpainting","score":0.8863797187805176},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8451606631278992},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6955171823501587},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.6594250798225403},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.5491963028907776},{"id":"https://openalex.org/keywords/annotation","display_name":"Annotation","score":0.5105472803115845},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.49199292063713074},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.48586294054985046},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4404502213001251},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4074052572250366},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.35449135303497314}],"concepts":[{"id":"https://openalex.org/C11727466","wikidata":"https://www.wikidata.org/wiki/Q1628157","display_name":"Inpainting","level":3,"score":0.8863797187805176},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8451606631278992},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6955171823501587},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.6594250798225403},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.5491963028907776},{"id":"https://openalex.org/C2776321320","wikidata":"https://www.wikidata.org/wiki/Q857525","display_name":"Annotation","level":2,"score":0.5105472803115845},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.49199292063713074},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.48586294054985046},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4404502213001251},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4074052572250366},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.35449135303497314},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"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/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1007/s11263-018-1132-0","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s11263-018-1132-0","pdf_url":"https://link.springer.com/content/pdf/10.1007/s11263-018-1132-0.pdf","source":{"id":"https://openalex.org/S25538012","display_name":"International Journal of Computer Vision","issn_l":"0920-5691","issn":["0920-5691","1573-1405"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Computer Vision","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1007/s11263-018-1132-0","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s11263-018-1132-0","pdf_url":"https://link.springer.com/content/pdf/10.1007/s11263-018-1132-0.pdf","source":{"id":"https://openalex.org/S25538012","display_name":"International Journal of Computer Vision","issn_l":"0920-5691","issn":["0920-5691","1573-1405"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Computer Vision","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education","score":0.6100000143051147}],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2902100912.pdf","grobid_xml":"https://content.openalex.org/works/W2902100912.grobid-xml"},"referenced_works_count":40,"referenced_works":["https://openalex.org/W69834426","https://openalex.org/W1834627138","https://openalex.org/W1975049209","https://openalex.org/W1981944020","https://openalex.org/W1993120651","https://openalex.org/W2001208767","https://openalex.org/W2001933992","https://openalex.org/W2050384301","https://openalex.org/W2054366734","https://openalex.org/W2057016804","https://openalex.org/W2066808422","https://openalex.org/W2073246097","https://openalex.org/W2078288556","https://openalex.org/W2093212899","https://openalex.org/W2096461046","https://openalex.org/W2097614539","https://openalex.org/W2105038642","https://openalex.org/W2105842272","https://openalex.org/W2108598243","https://openalex.org/W2113636985","https://openalex.org/W2123020895","https://openalex.org/W2136154655","https://openalex.org/W2147150922","https://openalex.org/W2151690846","https://openalex.org/W2292976057","https://openalex.org/W2352044167","https://openalex.org/W2431874326","https://openalex.org/W2513300417","https://openalex.org/W2732026016","https://openalex.org/W2893338698","https://openalex.org/W2963150697","https://openalex.org/W2963975576","https://openalex.org/W2988119488","https://openalex.org/W3043547428","https://openalex.org/W4249502209","https://openalex.org/W4249914127","https://openalex.org/W4252959399","https://openalex.org/W6676846806","https://openalex.org/W6678463710","https://openalex.org/W6740934225"],"related_works":["https://openalex.org/W3178025616","https://openalex.org/W2131831293","https://openalex.org/W2017457812","https://openalex.org/W2946160871","https://openalex.org/W1995073329","https://openalex.org/W3035059915","https://openalex.org/W2060947339","https://openalex.org/W425542480","https://openalex.org/W49967185","https://openalex.org/W2107727507"],"abstract_inverted_index":{"This":[0],"paper":[1],"proposes":[2],"a":[3,34,109],"learning-based":[4,46],"quality":[5,48],"evaluation":[6],"framework":[7,87],"for":[8,52,58,112,161],"inpainted":[9,95,131],"results":[10,37,93],"that":[11],"does":[12],"not":[13],"require":[14,54],"any":[15],"subjectively":[16,55],"annotated":[17,56],"training":[18,104,114,119],"data.":[19,105],"Image":[20],"inpainting,":[21],"which":[22],"removes":[23],"and":[24,66,89,154],"restores":[25],"unwanted":[26],"regions":[27],"in":[28,75],"images,":[29,132],"is":[30,137],"widely":[31],"acknowledged":[32],"as":[33,102],"task":[35,136],"whose":[36,97],"are":[38,100],"quite":[39,138],"difficult":[40],"to":[41,73,127],"evaluate":[42],"objectively.":[43],"Thus,":[44],"existing":[45,158],"image":[47],"assessment":[49],"(IQA)":[50],"methods":[51,160],"inpainting":[53],"data":[57,115,120],"training.":[59],"However,":[60],"subjective":[61,98],"annotation":[62],"requires":[63],"huge":[64],"cost":[65],"subjects\u2019":[67],"judgment":[68,79],"occasionally":[69],"differs":[70],"from":[71],"person":[72,74],"accordance":[76],"with":[77,151],"the":[78,85,103,135,142],"criteria.":[80],"To":[81,140],"overcome":[82],"these":[83],"difficulties,":[84],"proposed":[86],"generates":[88],"uses":[90],"simulated":[91],"failure":[92],"of":[94,144],"images":[96],"qualities":[99],"controlled":[101],"We":[106],"also":[107],"propose":[108],"masking":[110],"method":[111],"generating":[113],"towards":[116],"fully":[117],"automated":[118],"generation.":[121],"These":[122],"approaches":[123],"make":[124],"it":[125,156],"possible":[126],"successfully":[128],"estimate":[129],"better":[130],"even":[133],"though":[134],"subjective.":[139],"demonstrate":[141],"effectiveness":[143],"our":[145,149],"approach,":[146],"we":[147],"test":[148],"algorithm":[150],"various":[152],"datasets":[153],"show":[155],"outperforms":[157],"IQA":[159],"inpainting.":[162]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
