{"id":"https://openalex.org/W4413277660","doi":"https://doi.org/10.1109/icip55913.2025.11084500","title":"Pose Estimation of Artwork Characters with Series and Parallel Dilated Convolution And Style Channel Attention","display_name":"Pose Estimation of Artwork Characters with Series and Parallel Dilated Convolution And Style Channel Attention","publication_year":2025,"publication_date":"2025-08-18","ids":{"openalex":"https://openalex.org/W4413277660","doi":"https://doi.org/10.1109/icip55913.2025.11084500"},"language":"en","primary_location":{"id":"doi:10.1109/icip55913.2025.11084500","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip55913.2025.11084500","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Image Processing (ICIP)","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/A5068445056","display_name":"Tomoya Matsukawa","orcid":null},"institutions":[{"id":"https://openalex.org/I203951103","display_name":"Keio University","ror":"https://ror.org/02kn6nx58","country_code":"JP","type":"education","lineage":["https://openalex.org/I203951103"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Tomoya Matsukawa","raw_affiliation_strings":["Keio University,Yokohama,Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Keio University,Yokohama,Japan","institution_ids":["https://openalex.org/I203951103"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5113405527","display_name":"H. OGURA","orcid":null},"institutions":[{"id":"https://openalex.org/I203951103","display_name":"Keio University","ror":"https://ror.org/02kn6nx58","country_code":"JP","type":"education","lineage":["https://openalex.org/I203951103"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Hideyuki Ogura","raw_affiliation_strings":["Keio University,Yokohama,Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Keio University,Yokohama,Japan","institution_ids":["https://openalex.org/I203951103"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5008502235","display_name":"Shugo Yamashita","orcid":"https://orcid.org/0009-0003-8817-1057"},"institutions":[{"id":"https://openalex.org/I203951103","display_name":"Keio University","ror":"https://ror.org/02kn6nx58","country_code":"JP","type":"education","lineage":["https://openalex.org/I203951103"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Shugo Yamashita","raw_affiliation_strings":["Keio University,Yokohama,Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Keio University,Yokohama,Japan","institution_ids":["https://openalex.org/I203951103"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5005374921","display_name":"Kei Shibasaki","orcid":"https://orcid.org/0000-0002-7748-7246"},"institutions":[{"id":"https://openalex.org/I203951103","display_name":"Keio University","ror":"https://ror.org/02kn6nx58","country_code":"JP","type":"education","lineage":["https://openalex.org/I203951103"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Kei Shibasaki","raw_affiliation_strings":["Keio University,Yokohama,Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Keio University,Yokohama,Japan","institution_ids":["https://openalex.org/I203951103"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5090181402","display_name":"Masaaki Ikehara","orcid":"https://orcid.org/0000-0003-3461-1507"},"institutions":[{"id":"https://openalex.org/I203951103","display_name":"Keio University","ror":"https://ror.org/02kn6nx58","country_code":"JP","type":"education","lineage":["https://openalex.org/I203951103"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Masaaki Ikehara","raw_affiliation_strings":["Keio University,Yokohama,Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Keio University,Yokohama,Japan","institution_ids":["https://openalex.org/I203951103"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I203951103"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.20792861,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"857","last_page":"862"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10531","display_name":"Advanced Vision and Imaging","score":0.9800000190734863,"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/T10531","display_name":"Advanced Vision and Imaging","score":0.9800000190734863,"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/T10719","display_name":"3D Shape Modeling and Analysis","score":0.9793000221252441,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"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/T10481","display_name":"Computer Graphics and Visualization Techniques","score":0.9577000141143799,"subfield":{"id":"https://openalex.org/subfields/1704","display_name":"Computer Graphics and Computer-Aided Design"},"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/convolution","display_name":"Convolution (computer science)","score":0.7323164939880371},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7023614048957825},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.5862196087837219},{"id":"https://openalex.org/keywords/channel","display_name":"Channel (broadcasting)","score":0.585822582244873},{"id":"https://openalex.org/keywords/style","display_name":"Style (visual arts)","score":0.5115788578987122},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.46549901366233826},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4515940845012665},{"id":"https://openalex.org/keywords/computer-graphics","display_name":"Computer graphics (images)","score":0.3969844579696655},{"id":"https://openalex.org/keywords/art","display_name":"Art","score":0.13534298539161682},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.11730986833572388},{"id":"https://openalex.org/keywords/visual-arts","display_name":"Visual arts","score":0.10463607311248779},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.07353740930557251}],"concepts":[{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.7323164939880371},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7023614048957825},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.5862196087837219},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.585822582244873},{"id":"https://openalex.org/C2776445246","wikidata":"https://www.wikidata.org/wiki/Q1792644","display_name":"Style (visual arts)","level":2,"score":0.5115788578987122},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.46549901366233826},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4515940845012665},{"id":"https://openalex.org/C121684516","wikidata":"https://www.wikidata.org/wiki/Q7600677","display_name":"Computer graphics (images)","level":1,"score":0.3969844579696655},{"id":"https://openalex.org/C142362112","wikidata":"https://www.wikidata.org/wiki/Q735","display_name":"Art","level":0,"score":0.13534298539161682},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.11730986833572388},{"id":"https://openalex.org/C153349607","wikidata":"https://www.wikidata.org/wiki/Q36649","display_name":"Visual arts","level":1,"score":0.10463607311248779},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.07353740930557251},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icip55913.2025.11084500","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip55913.2025.11084500","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W1861492603","https://openalex.org/W2080873731","https://openalex.org/W2097117768","https://openalex.org/W2531409750","https://openalex.org/W2559085405","https://openalex.org/W2573563386","https://openalex.org/W2752782242","https://openalex.org/W2962820842","https://openalex.org/W2962834855","https://openalex.org/W2963150697","https://openalex.org/W2963163009","https://openalex.org/W2963918968","https://openalex.org/W2964304707","https://openalex.org/W3014641072","https://openalex.org/W3173811519","https://openalex.org/W3188937449","https://openalex.org/W4289752563","https://openalex.org/W4308933760","https://openalex.org/W4313127332","https://openalex.org/W4386075664","https://openalex.org/W4403417347"],"related_works":["https://openalex.org/W2356229341","https://openalex.org/W2349768204","https://openalex.org/W1919101720","https://openalex.org/W4313326281","https://openalex.org/W574867512","https://openalex.org/W2387271333","https://openalex.org/W581389233","https://openalex.org/W2361120309","https://openalex.org/W632157940","https://openalex.org/W2387981414"],"abstract_inverted_index":{"2D":[0],"pose":[1],"estimation":[2],"is":[3,56,83],"a":[4,13,48],"fundamental":[5],"task":[6],"predicting":[7],"geometric":[8],"construction":[9],"of":[10,37,63,110,126],"targets":[11],"like":[12],"human":[14],"and":[15,34,51,77,90,94,124,140],"an":[16,86,111,151],"animal":[17],"from":[18],"input":[19,112],"images.":[20],"Notably,":[21],"we":[22,69,100],"focus":[23],"on":[24,29,85],"characters":[25,39],"that":[26,71],"are":[27],"drawn":[28],"canvas,":[30],"such":[31,46],"as":[32,47],"illustrations":[33],"paintings.":[35],"Poses":[36],"the":[38,60,148],"can":[40,118],"be":[41],"used":[42],"for":[43],"many":[44],"applications,":[45],"retrieval":[49],"system":[50],"detecting":[52],"plagiarism.":[53],"However,":[54],"it":[55],"difficult":[57],"to":[58],"identify":[59],"poses":[61],"because":[62],"domain":[64,72],"characteristics.":[65],"In":[66,98],"this":[67],"paper,":[68],"tackle":[70],"issue":[73],"by":[74,137,143],"developing":[75],"Series":[76],"Parallel":[78],"Dilated":[79],"Convolution":[80],"(SPDC),":[81],"which":[82,106],"based":[84],"inverted":[87],"residual":[88],"bottleneck":[89],"has":[91],"cascade":[92],"connections":[93,96],"parallel":[95],"simultaneously.":[97],"addition,":[99],"propose":[101],"Style":[102],"Channel":[103],"Attention":[104],"(SCA),":[105],"reflects":[107],"style":[108],"information":[109],"image":[113],"in":[114,146],"estimation.":[115],"These":[116],"modules":[117],"deal":[119],"with":[120,129,150],"different":[121],"painting":[122],"styles":[123],"parts":[125],"characters.":[127],"Compared":[128],"existing":[130],"methods,":[131],"our":[132],"method":[133],"improves":[134],"average":[135,141],"precision":[136],"2.89":[138],"%":[139,145],"recall":[142],"2.58":[144],"evaluating":[147],"results":[149],"illustration":[152],"dataset.":[153]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
