{"id":"https://openalex.org/W4323338556","doi":"https://doi.org/10.1109/access.2023.3253206","title":"Pareto Optimized Large Mask Approach for Efficient and Background Humanoid Shape Removal","display_name":"Pareto Optimized Large Mask Approach for Efficient and Background Humanoid Shape Removal","publication_year":2023,"publication_date":"2023-01-01","ids":{"openalex":"https://openalex.org/W4323338556","doi":"https://doi.org/10.1109/access.2023.3253206"},"language":"en","primary_location":{"id":"doi:10.1109/access.2023.3253206","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2023.3253206","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1109/access.2023.3253206","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5039508726","display_name":"Rytis Maskeli\u016bnas","orcid":"https://orcid.org/0000-0002-2809-2213"},"institutions":[{"id":"https://openalex.org/I119004910","display_name":"Silesian University of Technology","ror":"https://ror.org/02dyjk442","country_code":"PL","type":"education","lineage":["https://openalex.org/I119004910"]},{"id":"https://openalex.org/I172574986","display_name":"Kaunas University of Technology","ror":"https://ror.org/01me6gb93","country_code":"LT","type":"education","lineage":["https://openalex.org/I172574986"]}],"countries":["LT","PL"],"is_corresponding":true,"raw_author_name":"Rytis Maskeliunas","raw_affiliation_strings":["Center of Excellence Forest 4.0, Faculty of Informatics, Kaunas University of Technology, Kaunas, Lithuania","Faculty of Applied Mathematics, Silesian University of Technology, Gliwice, Poland","Faculty of Informatics, Center of Excellence Forest 4.0, Kaunas University of Technology, Kaunas, Lithuania"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Center of Excellence Forest 4.0, Faculty of Informatics, Kaunas University of Technology, Kaunas, Lithuania","institution_ids":["https://openalex.org/I172574986"]},{"raw_affiliation_string":"Faculty of Applied Mathematics, Silesian University of Technology, Gliwice, Poland","institution_ids":["https://openalex.org/I119004910"]},{"raw_affiliation_string":"Faculty of Informatics, Center of Excellence Forest 4.0, Kaunas University of Technology, Kaunas, Lithuania","institution_ids":["https://openalex.org/I172574986"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5042653526","display_name":"Robertas Dama\u0161evi\u010dius","orcid":"https://orcid.org/0000-0001-9990-1084"},"institutions":[{"id":"https://openalex.org/I119004910","display_name":"Silesian University of Technology","ror":"https://ror.org/02dyjk442","country_code":"PL","type":"education","lineage":["https://openalex.org/I119004910"]},{"id":"https://openalex.org/I188730046","display_name":"Vytautas Magnus University","ror":"https://ror.org/04y7eh037","country_code":"LT","type":"education","lineage":["https://openalex.org/I188730046"]}],"countries":["LT","PL"],"is_corresponding":false,"raw_author_name":"Robertas Dama\u0161evi\u010dius","raw_affiliation_strings":["Faculty of Applied Mathematics, Silesian University of Technology, Gliwice, Poland","Department of Applied Informatics, Center of Excellence Forest 4.0, Vytautas Magnus University, Kaunas, Lithuania"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Faculty of Applied Mathematics, Silesian University of Technology, Gliwice, Poland","institution_ids":["https://openalex.org/I119004910"]},{"raw_affiliation_string":"Department of Applied Informatics, Center of Excellence Forest 4.0, Vytautas Magnus University, Kaunas, Lithuania","institution_ids":["https://openalex.org/I188730046"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5046551434","display_name":"Daiva Vitkut\u0117-Ad\u017egauskien\u0117","orcid":"https://orcid.org/0000-0001-7923-1087"},"institutions":[{"id":"https://openalex.org/I188730046","display_name":"Vytautas Magnus University","ror":"https://ror.org/04y7eh037","country_code":"LT","type":"education","lineage":["https://openalex.org/I188730046"]}],"countries":["LT"],"is_corresponding":false,"raw_author_name":"Daiva Vitkute-Adzgauskiene","raw_affiliation_strings":["Department of Applied Informatics, Center of Excellence Forest 4.0, Vytautas Magnus University, Kaunas, Lithuania"],"raw_orcid":"https://orcid.org/0000-0001-7923-1087","affiliations":[{"raw_affiliation_string":"Department of Applied Informatics, Center of Excellence Forest 4.0, Vytautas Magnus University, Kaunas, Lithuania","institution_ids":["https://openalex.org/I188730046"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5064136287","display_name":"Sanjay Misra","orcid":"https://orcid.org/0000-0002-3556-9331"},"institutions":[{"id":"https://openalex.org/I19923696","display_name":"\u00d8stfold University College","ror":"https://ror.org/04gf7fp41","country_code":"NO","type":"education","lineage":["https://openalex.org/I19923696"]}],"countries":["NO"],"is_corresponding":false,"raw_author_name":"Sanjay Misra","raw_affiliation_strings":["Department of Computer Science and Communication, Ostfold University College, Halden, Norway"],"raw_orcid":"https://orcid.org/0000-0002-3556-9331","affiliations":[{"raw_affiliation_string":"Department of Computer Science and Communication, Ostfold University College, Halden, Norway","institution_ids":["https://openalex.org/I19923696"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5039508726"],"corresponding_institution_ids":["https://openalex.org/I119004910","https://openalex.org/I172574986"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.1158,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.35731512,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":"11","issue":null,"first_page":"33900","last_page":"33914"},"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":0.9980999827384949,"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":0.9980999827384949,"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":0.9958999752998352,"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/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.9932000041007996,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7926561832427979},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7467577457427979},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.6181602478027344},{"id":"https://openalex.org/keywords/inpainting","display_name":"Inpainting","score":0.5148488879203796},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.45932504534721375},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.44812270998954773},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.4411777853965759},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4356768727302551},{"id":"https://openalex.org/keywords/discriminator","display_name":"Discriminator","score":0.4184603989124298},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.32607191801071167}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7926561832427979},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7467577457427979},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6181602478027344},{"id":"https://openalex.org/C11727466","wikidata":"https://www.wikidata.org/wiki/Q1628157","display_name":"Inpainting","level":3,"score":0.5148488879203796},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.45932504534721375},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.44812270998954773},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.4411777853965759},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4356768727302551},{"id":"https://openalex.org/C2779803651","wikidata":"https://www.wikidata.org/wiki/Q5282088","display_name":"Discriminator","level":3,"score":0.4184603989124298},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.32607191801071167},{"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}],"mesh":[],"locations_count":5,"locations":[{"id":"doi:10.1109/access.2023.3253206","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2023.3253206","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:ktu.edu:elaba:158676178","is_oa":true,"landing_page_url":"https://vb.ktu.edu/KTU:ELABAPDB158676178&prefLang=en_US","pdf_url":null,"source":{"id":"https://openalex.org/S4406922255","display_name":"KTUePubl (Repository of Kaunas University of Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"ISSN 2169-3536","raw_type":"info:eu-repo/semantics/article"},{"id":"pmh:oai:doaj.org/article:258adf4ff2af440b9663ea080e77495c","is_oa":true,"landing_page_url":"https://doaj.org/article/258adf4ff2af440b9663ea080e77495c","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 11, Pp 33900-33914 (2023)","raw_type":"article"},{"id":"pmh:oai:hiof.brage.unit.no:11250/3116204","is_oa":true,"landing_page_url":"https://hdl.handle.net/11250/3116204","pdf_url":null,"source":{"id":"https://openalex.org/S4306401716","display_name":"Duo Research Archive (University of Oslo)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I184942183","host_organization_name":"University of Oslo","host_organization_lineage":["https://openalex.org/I184942183"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"33900-33914","raw_type":"info:eu-repo/semantics/other"},{"id":"pmh:oai:vdu.lt:20.500.12259/249302","is_oa":false,"landing_page_url":"https://hdl.handle.net/20.500.12259/249302","pdf_url":null,"source":{"id":"https://openalex.org/S7407055361","display_name":"Vytautas Magnus University","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"research article"}],"best_oa_location":{"id":"doi:10.1109/access.2023.3253206","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2023.3253206","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Reduced inequalities","score":0.6700000166893005,"id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":120,"referenced_works":["https://openalex.org/W1986610313","https://openalex.org/W2017466994","https://openalex.org/W2017906503","https://openalex.org/W2029411311","https://openalex.org/W2047422818","https://openalex.org/W2048053649","https://openalex.org/W2071080980","https://openalex.org/W2089942311","https://openalex.org/W2096461046","https://openalex.org/W2105038642","https://openalex.org/W2128319034","https://openalex.org/W2133665775","https://openalex.org/W2144245917","https://openalex.org/W2168878035","https://openalex.org/W2296495263","https://openalex.org/W2470139095","https://openalex.org/W2546594897","https://openalex.org/W2557414982","https://openalex.org/W2613645387","https://openalex.org/W2619748849","https://openalex.org/W2767972054","https://openalex.org/W2774889464","https://openalex.org/W2785413559","https://openalex.org/W2790965620","https://openalex.org/W2798365772","https://openalex.org/W2808402081","https://openalex.org/W2889986507","https://openalex.org/W2890447039","https://openalex.org/W2896434438","https://openalex.org/W2901428773","https://openalex.org/W2903566909","https://openalex.org/W2907097116","https://openalex.org/W2916743882","https://openalex.org/W2942930375","https://openalex.org/W2953735693","https://openalex.org/W2962555351","https://openalex.org/W2962785568","https://openalex.org/W2963231084","https://openalex.org/W2963420272","https://openalex.org/W2963713691","https://openalex.org/W2964148878","https://openalex.org/W2965570359","https://openalex.org/W2969109746","https://openalex.org/W2969925510","https://openalex.org/W2977027504","https://openalex.org/W2978704794","https://openalex.org/W2981682056","https://openalex.org/W2982723417","https://openalex.org/W2982763192","https://openalex.org/W2986433113","https://openalex.org/W2989207674","https://openalex.org/W2991377405","https://openalex.org/W2992838650","https://openalex.org/W2997729188","https://openalex.org/W2998075999","https://openalex.org/W2999351684","https://openalex.org/W3011955763","https://openalex.org/W3027273019","https://openalex.org/W3028094643","https://openalex.org/W3034457131","https://openalex.org/W3034482833","https://openalex.org/W3035016310","https://openalex.org/W3035512475","https://openalex.org/W3039136639","https://openalex.org/W3048475513","https://openalex.org/W3082900749","https://openalex.org/W3091185377","https://openalex.org/W3093591919","https://openalex.org/W3101045767","https://openalex.org/W3102846047","https://openalex.org/W3105389099","https://openalex.org/W3105938520","https://openalex.org/W3108554146","https://openalex.org/W3108556207","https://openalex.org/W3118356434","https://openalex.org/W3120987907","https://openalex.org/W3135133849","https://openalex.org/W3136322089","https://openalex.org/W3137564086","https://openalex.org/W3151493227","https://openalex.org/W3152856895","https://openalex.org/W3157141280","https://openalex.org/W3159937784","https://openalex.org/W3166737521","https://openalex.org/W3168686693","https://openalex.org/W3169506151","https://openalex.org/W3171933509","https://openalex.org/W3174754903","https://openalex.org/W3175375202","https://openalex.org/W3176270734","https://openalex.org/W3184671611","https://openalex.org/W3188813591","https://openalex.org/W3195253653","https://openalex.org/W3199003182","https://openalex.org/W3200489710","https://openalex.org/W3200611399","https://openalex.org/W3203035638","https://openalex.org/W3203538104","https://openalex.org/W3204170321","https://openalex.org/W4214736933","https://openalex.org/W4221004921","https://openalex.org/W4221132069","https://openalex.org/W4231624225","https://openalex.org/W4283462010","https://openalex.org/W4287755748","https://openalex.org/W4292794843","https://openalex.org/W4295012191","https://openalex.org/W4296334467","https://openalex.org/W4301054805","https://openalex.org/W4304098301","https://openalex.org/W4312655706","https://openalex.org/W6735925877","https://openalex.org/W6748645028","https://openalex.org/W6754033419","https://openalex.org/W6761973954","https://openalex.org/W6765779288","https://openalex.org/W6779739588","https://openalex.org/W6795876889","https://openalex.org/W6810232963","https://openalex.org/W6839667351"],"related_works":["https://openalex.org/W2059339452","https://openalex.org/W2262668847","https://openalex.org/W2995115364","https://openalex.org/W2020564930","https://openalex.org/W1574999717","https://openalex.org/W2370766994","https://openalex.org/W166251047","https://openalex.org/W3203509158","https://openalex.org/W3195987654","https://openalex.org/W4214647905"],"abstract_inverted_index":{"We":[0],"introduce":[1],"deep":[2],"learning-based":[3],"methodology":[4],"for":[5,35],"removing":[6],"unwanted":[7],"human-like":[8],"shapes":[9,58,179],"in":[10,233,264],"videos.":[11],"The":[12,26,52,79,130,150,184,207,237],"method":[13,208],"uses":[14,40,152],"Pareto-optimized":[15],"Generative":[16],"Adversarial":[17],"Networks":[18],"(GANs)":[19],"technology,":[20],"which":[21,47],"is":[22,107,209],"a":[23,41,62,111,221,231],"novel":[24],"contribution.":[25],"system":[27],"automatically":[28],"selects":[29],"the":[30,87,94,114,134,138,146,174,177,190,194,200,242],"Region":[31],"of":[32,55,202,223,228],"Interest":[33],"(ROI)":[34],"each":[36,203],"humanoid":[37,48,148,178,205],"shape":[38,49],"and":[39,73,76,120,124,137,164,167,169,193,218,220,252,260],"skeleton":[42],"detection":[43],"module":[44],"to":[45,50,85,91,109,140,160,172],"determine":[46],"retain.":[51],"semantic":[53,77],"masks":[54],"human":[56],"like":[57],"are":[59],"created":[60],"using":[61,241],"semantic-aware":[63],"occlusion-robust":[64],"model":[65],"that":[66],"has":[67],"four":[68],"primary":[69],"components:":[70],"feature":[71],"extraction,":[72],"local,":[74],"global,":[75],"branches.":[78],"global":[80],"branch":[81,96],"encodes":[82],"occlusion-aware":[83],"information":[84,163],"make":[86,173],"extracted":[88],"features":[89,198],"resistant":[90],"occlusion,":[92],"while":[93],"local":[95,99],"retrieves":[97],"fine-grained":[98],"characteristics.":[100],"A":[101],"modified":[102],"big":[103],"mask":[104,139],"inpainting":[105],"approach":[106],"employed":[108],"eliminate":[110],"person":[112,268],"from":[113],"image,":[115],"leveraging":[116],"Fast":[117],"Fourier":[118],"convolutions":[119],"utilizing":[121],"polygonal":[122],"chains":[123],"rectangles":[125],"with":[126,156,181],"unpredictable":[127],"aspect":[128],"ratios.":[129],"inpainter":[131],"network":[132,196],"takes":[133],"input":[135],"image":[136,144],"create":[141],"an":[142,153,234],"output":[143],"excluding":[145],"background":[147,204,267],"shapes.":[149],"generator":[151],"encoder-decoder":[154],"structure":[155,188],"included":[157],"skip":[158],"connections":[159],"recover":[161],"spatial":[162],"dilated":[165],"convolution":[166],"squeeze":[168],"excitation":[170],"blocks":[171],"regions":[175],"behind":[176,230],"consistent":[180],"their":[182],"surroundings.":[183],"discriminator":[185],"avoids":[186],"dissimilar":[187],"at":[189],"patch":[191],"scale,":[192],"refiner":[195],"catches":[197],"around":[199],"boundaries":[201],"shape.":[206],"evaluated":[210],"on":[211],"two":[212],"video":[213,226],"object":[214],"segmentation":[215],"datasets":[216],"(DAVIS":[217],"YouTube-VOS)":[219],"database":[222],"66":[224],"distinct":[225],"sequences":[227],"people":[229],"desk":[232],"office":[235],"environment.":[236],"efficiency":[238],"was":[239],"assessed":[240],"Structural":[243],"Similarity":[244,257],"Index":[245],"Measure":[246],"(SSIM),":[247],"Frechet":[248],"Inception":[249],"Distance":[250],"(FID),":[251],"Learned":[253],"Perceptual":[254],"Image":[255],"Patch":[256],"(LPIPS)":[258],"metrics":[259],"showed":[261],"promising":[262],"results":[263],"fully":[265],"automated":[266],"removal":[269],"task.":[270]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-05-06T08:25:59.206177","created_date":"2025-10-10T00:00:00"}