{"id":"https://openalex.org/W7131094030","doi":"https://doi.org/10.1109/iccvw69036.2025.00047","title":"WP-CLIP: Leveraging CLIP to Predict W\u00f6lfflin's Principles in Visual Art","display_name":"WP-CLIP: Leveraging CLIP to Predict W\u00f6lfflin's Principles in Visual Art","publication_year":2025,"publication_date":"2025-10-19","ids":{"openalex":"https://openalex.org/W7131094030","doi":"https://doi.org/10.1109/iccvw69036.2025.00047"},"language":null,"primary_location":{"id":"doi:10.1109/iccvw69036.2025.00047","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccvw69036.2025.00047","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)","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/A5022978908","display_name":"Abhijay Ghildyal","orcid":"https://orcid.org/0000-0003-1940-9626"},"institutions":[{"id":"https://openalex.org/I126345244","display_name":"Portland State University","ror":"https://ror.org/00yn2fy02","country_code":"US","type":"education","lineage":["https://openalex.org/I126345244"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Abhijay Ghildyal","raw_affiliation_strings":["Portland State University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Portland State University","institution_ids":["https://openalex.org/I126345244"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100760596","display_name":"Liyun Wang","orcid":"https://orcid.org/0000-0003-4288-2569"},"institutions":[{"id":"https://openalex.org/I126345244","display_name":"Portland State University","ror":"https://ror.org/00yn2fy02","country_code":"US","type":"education","lineage":["https://openalex.org/I126345244"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Li-Yun Wang","raw_affiliation_strings":["Portland State University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Portland State University","institution_ids":["https://openalex.org/I126345244"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5085075897","display_name":"Feng Liu","orcid":"https://orcid.org/0000-0003-3383-7598"},"institutions":[{"id":"https://openalex.org/I126345244","display_name":"Portland State University","ror":"https://ror.org/00yn2fy02","country_code":"US","type":"education","lineage":["https://openalex.org/I126345244"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Feng Liu","raw_affiliation_strings":["Portland State University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Portland State University","institution_ids":["https://openalex.org/I126345244"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I126345244"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"396","last_page":"405"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12650","display_name":"Aesthetic Perception and Analysis","score":0.8927000164985657,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},"topics":[{"id":"https://openalex.org/T12650","display_name":"Aesthetic Perception and Analysis","score":0.8927000164985657,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T13342","display_name":"Art History and Market Analysis","score":0.012600000016391277,"subfield":{"id":"https://openalex.org/subfields/1213","display_name":"Visual Arts and Performing Arts"},"field":{"id":"https://openalex.org/fields/12","display_name":"Arts and Humanities"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10799","display_name":"Data Visualization and Analytics","score":0.010300000198185444,"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/metric","display_name":"Metric (unit)","score":0.6317999958992004},{"id":"https://openalex.org/keywords/painting","display_name":"Painting","score":0.5906999707221985},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.4943000078201294},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.400299996137619},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.3237999975681305},{"id":"https://openalex.org/keywords/visualization","display_name":"Visualization","score":0.3221000134944916}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6848999857902527},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.6317999958992004},{"id":"https://openalex.org/C205783811","wikidata":"https://www.wikidata.org/wiki/Q11629","display_name":"Painting","level":2,"score":0.5906999707221985},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5501000285148621},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.4943000078201294},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.400299996137619},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3237999975681305},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.3221000134944916},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.31690001487731934},{"id":"https://openalex.org/C66024118","wikidata":"https://www.wikidata.org/wiki/Q1122506","display_name":"Computational model","level":2,"score":0.28060001134872437},{"id":"https://openalex.org/C2780878386","wikidata":"https://www.wikidata.org/wiki/Q1659648","display_name":"Visual language","level":2,"score":0.27880001068115234},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.27799999713897705},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.2736999988555908},{"id":"https://openalex.org/C93692415","wikidata":"https://www.wikidata.org/wiki/Q1502030","display_name":"Thematic map","level":2,"score":0.2671000063419342},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.2612000107765198}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iccvw69036.2025.00047","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccvw69036.2025.00047","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)","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":25,"referenced_works":["https://openalex.org/W132232344","https://openalex.org/W2030353835","https://openalex.org/W2078807908","https://openalex.org/W2613527656","https://openalex.org/W2884238566","https://openalex.org/W2888817066","https://openalex.org/W2937703861","https://openalex.org/W2963370915","https://openalex.org/W2984215311","https://openalex.org/W3024328483","https://openalex.org/W3035574324","https://openalex.org/W3129083428","https://openalex.org/W3145527391","https://openalex.org/W3160097840","https://openalex.org/W3180353325","https://openalex.org/W4283817757","https://openalex.org/W4291801459","https://openalex.org/W4296900456","https://openalex.org/W4382462760","https://openalex.org/W4391405920","https://openalex.org/W4401307360","https://openalex.org/W4402716477","https://openalex.org/W4402915703","https://openalex.org/W4409658727","https://openalex.org/W4410706519"],"related_works":[],"abstract_inverted_index":{"W\u00f6lfflin's":[0,86],"five":[1,21],"principles":[2,22],"offer":[3],"a":[4,32,35,116],"structured":[5],"approach":[6],"to":[7,59,114,137],"analyzing":[8],"stylistic":[9,99],"variations":[10],"for":[11,68,118,150],"formal":[12],"analysis.":[13,153],"However,":[14],"no":[15],"existing":[16],"metric":[17,36],"effectively":[18],"predicts":[19],"all":[20],"in":[23,51],"visual":[24,29],"art.":[25],"Computationally":[26],"evaluating":[27],"the":[28,130,146],"aspects":[30],"of":[31,110,148],"painting":[33],"requires":[34],"that":[37,91],"can":[38,82],"interpret":[39],"key":[40],"elements":[41],"such":[42,97],"as":[43],"color,":[44],"composition,":[45],"and":[46,84,129],"thematic":[47],"choices.":[48],"Recent":[49],"advancements":[50],"vision-language":[52],"models":[53],"(VLMs)":[54],"have":[55],"demonstrated":[56],"their":[57],"ability":[58,136],"evaluate":[60,122],"abstract":[61],"image":[62],"attributes,":[63],"making":[64],"them":[65],"promising":[66],"candidates":[67],"this":[69,72],"task.":[70],"In":[71],"work,":[73],"we":[74,104],"investigate":[75],"whether":[76],"CLIP,":[77],"pre-trained":[78],"on":[79,107,126],"large-scale":[80],"data,":[81],"understand":[83],"predict":[85,115],"principles.":[87],"Our":[88,143],"findings":[89],"indicate":[90],"it":[92],"does":[93],"not":[94],"inherently":[95],"capture":[96],"nuanced":[98],"elements.":[100],"To":[101],"address":[102],"this,":[103],"fine-tune":[105],"CLIP":[106],"annotated":[108],"datasets":[109],"real":[111],"art":[112,132,152],"images":[113],"score":[117],"each":[119],"principle.":[120],"We":[121],"our":[123],"model,":[124],"WP-CLIP,":[125],"GAN-generated":[127],"paintings":[128],"Pandora-18K":[131],"dataset,":[133],"demonstrating":[134],"its":[135],"generalize":[138],"across":[139],"diverse":[140],"artistic":[141],"styles.":[142],"results":[144],"highlight":[145],"potential":[147],"VLMs":[149],"automated":[151],"Code:":[154],"https://github.com/abhijay9/wpclip":[155]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2026-02-24T00:00:00"}