{"id":"https://openalex.org/W4402352967","doi":"https://doi.org/10.1109/ijcnn60899.2024.10651034","title":"Inpainting-Driven Mask Optimization for Object Removal","display_name":"Inpainting-Driven Mask Optimization for Object Removal","publication_year":2024,"publication_date":"2024-06-30","ids":{"openalex":"https://openalex.org/W4402352967","doi":"https://doi.org/10.1109/ijcnn60899.2024.10651034"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn60899.2024.10651034","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn60899.2024.10651034","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5032877617","display_name":"Kodai Shimosato","orcid":null},"institutions":[{"id":"https://openalex.org/I4840577","display_name":"Toyota Technological Institute","ror":"https://ror.org/001hv0k59","country_code":"JP","type":"education","lineage":["https://openalex.org/I4840577"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Kodai Shimosato","raw_affiliation_strings":["Toyota Technological Institute,Nagoya,Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Toyota Technological Institute,Nagoya,Japan","institution_ids":["https://openalex.org/I4840577"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5053167635","display_name":"Norimichi Ukita","orcid":"https://orcid.org/0000-0002-0240-1065"},"institutions":[{"id":"https://openalex.org/I4840577","display_name":"Toyota Technological Institute","ror":"https://ror.org/001hv0k59","country_code":"JP","type":"education","lineage":["https://openalex.org/I4840577"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Norimichi Ukita","raw_affiliation_strings":["Toyota Technological Institute,Nagoya,Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Toyota Technological Institute,Nagoya,Japan","institution_ids":["https://openalex.org/I4840577"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I4840577"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":null,"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/T11338","display_name":"Advancements in Photolithography Techniques","score":0.98580002784729,"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/T11338","display_name":"Advancements in Photolithography Techniques","score":0.98580002784729,"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/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.9843000173568726,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing 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/T10036","display_name":"Advanced Neural Network Applications","score":0.9829000234603882,"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":0.8304388523101807},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6686580181121826},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6290398836135864},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.6005537509918213},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.5989367961883545},{"id":"https://openalex.org/keywords/computer-graphics","display_name":"Computer graphics (images)","score":0.3505058288574219},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.2656899094581604}],"concepts":[{"id":"https://openalex.org/C11727466","wikidata":"https://www.wikidata.org/wiki/Q1628157","display_name":"Inpainting","level":3,"score":0.8304388523101807},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6686580181121826},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6290398836135864},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6005537509918213},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.5989367961883545},{"id":"https://openalex.org/C121684516","wikidata":"https://www.wikidata.org/wiki/Q7600677","display_name":"Computer graphics (images)","level":1,"score":0.3505058288574219},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.2656899094581604}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn60899.2024.10651034","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn60899.2024.10651034","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":59,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1861492603","https://openalex.org/W2155597438","https://openalex.org/W2288997049","https://openalex.org/W2331128040","https://openalex.org/W2395611524","https://openalex.org/W2475287302","https://openalex.org/W2779331624","https://openalex.org/W2794849006","https://openalex.org/W2798365772","https://openalex.org/W2801693445","https://openalex.org/W2806070179","https://openalex.org/W2883820570","https://openalex.org/W2948627722","https://openalex.org/W2950862326","https://openalex.org/W2962785568","https://openalex.org/W2963271314","https://openalex.org/W2963420272","https://openalex.org/W2964148878","https://openalex.org/W2964193438","https://openalex.org/W2966434031","https://openalex.org/W2981682056","https://openalex.org/W2982763192","https://openalex.org/W2988407809","https://openalex.org/W2989207674","https://openalex.org/W2989505547","https://openalex.org/W2990886896","https://openalex.org/W2991377405","https://openalex.org/W2997729188","https://openalex.org/W2998075999","https://openalex.org/W2999219213","https://openalex.org/W3034419329","https://openalex.org/W3035358681","https://openalex.org/W3035512475","https://openalex.org/W3043547428","https://openalex.org/W3106725490","https://openalex.org/W3109174731","https://openalex.org/W3132455321","https://openalex.org/W3136424010","https://openalex.org/W3168649818","https://openalex.org/W3176659256","https://openalex.org/W3195866886","https://openalex.org/W3202418656","https://openalex.org/W3206082266","https://openalex.org/W4292794826","https://openalex.org/W4301206121","https://openalex.org/W4312238440","https://openalex.org/W4312328737","https://openalex.org/W4312383858","https://openalex.org/W4312450015","https://openalex.org/W4312933868","https://openalex.org/W4388145647","https://openalex.org/W4392567191","https://openalex.org/W6631190155","https://openalex.org/W6683590716","https://openalex.org/W6741563403","https://openalex.org/W6750942122","https://openalex.org/W6752437260","https://openalex.org/W6765779288"],"related_works":["https://openalex.org/W2135359786","https://openalex.org/W2058170566","https://openalex.org/W2755342338","https://openalex.org/W2772917594","https://openalex.org/W2775347418","https://openalex.org/W2166024367","https://openalex.org/W3116076068","https://openalex.org/W2229312674","https://openalex.org/W2951359407","https://openalex.org/W2079911747"],"abstract_inverted_index":{"This":[0,45],"paper":[1],"proposes":[2],"a":[3,24,29,39],"mask":[4,128],"optimization":[5],"method":[6,147],"for":[7,31,98,131,148],"improving":[8],"the":[9,56,59,72,89,95,100,106,114,133,143],"quality":[10],"of":[11,26,58,119,145],"object":[12,76,83,96,150],"removal":[13,151],"using":[14,152],"image":[15,153],"inpainting.":[16,154],"While":[17],"many":[18,42],"inpainting":[19,32,60,73,107,115],"methods":[20],"are":[21,85],"trained":[22,111],"with":[23,75],"set":[25],"random":[27],"masks,":[28],"target":[30],"may":[33],"be":[34],"an":[35],"object,":[36],"such":[37,82],"as":[38],"person,":[40],"in":[41,50,88],"realistic":[43],"scenarios.":[44],"domain":[46,66],"gap":[47,67],"between":[48,135],"masks":[49,77,84,97],"training":[51,71,122],"and":[52,81,109,137],"inference":[53,90],"images":[54],"increases":[55],"difficulty":[57],"task.":[61],"In":[62],"our":[63,127,146],"method,":[64],"this":[65,120],"is":[68,103,123],"resolved":[69],"by":[70,79,126],"network":[74,102,108],"extracted":[78],"segmentation,":[80],"also":[86],"used":[87],"step.":[91],"Furthermore,":[92],"to":[93,105,112],"optimize":[94],"inpainting,":[99],"segmentation":[101],"connected":[104],"end-to-end":[110,121],"improve":[113],"performance.":[116],"The":[117],"effect":[118],"further":[124],"enhanced":[125],"expansion":[129],"loss":[130],"achieving":[132],"trade-off":[134],"large":[136],"small":[138],"masks.":[139],"Experimental":[140],"results":[141],"demonstrate":[142],"effectiveness":[144],"better":[149]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}