{"id":"https://openalex.org/W7162625753","doi":"https://doi.org/10.1145/3774905.3795446","title":"ImVision: Adapting Pretrained Vision Models for Time-Series Imputation","display_name":"ImVision: Adapting Pretrained Vision Models for Time-Series Imputation","publication_year":2026,"publication_date":"2026-05-28","ids":{"openalex":"https://openalex.org/W7162625753","doi":"https://doi.org/10.1145/3774905.3795446"},"language":null,"primary_location":{"id":"doi:10.1145/3774905.3795446","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3774905.3795446","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Companion Proceedings of the ACM Web Conference 2026","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3774905.3795446","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5137198225","display_name":"Alireza Shammasi","orcid":"https://orcid.org/0009-0001-1870-0705"},"institutions":[{"id":"https://openalex.org/I99043593","display_name":"Macquarie University","ror":"https://ror.org/01sf06y89","country_code":"AU","type":"education","lineage":["https://openalex.org/I99043593"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Alireza Shammasi","raw_affiliation_strings":["Macquarie University, Sydney, New South Wales, Australia"],"raw_orcid":"https://orcid.org/0009-0001-1870-0705","affiliations":[{"raw_affiliation_string":"Macquarie University, Sydney, New South Wales, Australia","institution_ids":["https://openalex.org/I99043593"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137247174","display_name":"Amin Beheshti","orcid":"https://orcid.org/0000-0002-5988-5494"},"institutions":[{"id":"https://openalex.org/I99043593","display_name":"Macquarie University","ror":"https://ror.org/01sf06y89","country_code":"AU","type":"education","lineage":["https://openalex.org/I99043593"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Amin Beheshti","raw_affiliation_strings":["Macquarie University, Sydney, New South Wales, Australia"],"raw_orcid":"https://orcid.org/0000-0002-5988-5494","affiliations":[{"raw_affiliation_string":"Macquarie University, Sydney, New South Wales, Australia","institution_ids":["https://openalex.org/I99043593"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5117649500","display_name":"Milad Mosharraf","orcid":"https://orcid.org/0009-0002-3133-7960"},"institutions":[{"id":"https://openalex.org/I1329868518","display_name":"NSW Department of Education","ror":"https://ror.org/05nne8c43","country_code":"AU","type":"funder","lineage":["https://openalex.org/I1329868518","https://openalex.org/I2801351115"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Milad Mosharraf","raw_affiliation_strings":["Domain Group, Sydney, New South Wales, Australia"],"raw_orcid":"https://orcid.org/0009-0002-3133-7960","affiliations":[{"raw_affiliation_string":"Domain Group, Sydney, New South Wales, Australia","institution_ids":["https://openalex.org/I1329868518"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137234551","display_name":"Min Li","orcid":"https://orcid.org/0009-0002-5085-9289"},"institutions":[{"id":"https://openalex.org/I1329868518","display_name":"NSW Department of Education","ror":"https://ror.org/05nne8c43","country_code":"AU","type":"funder","lineage":["https://openalex.org/I1329868518","https://openalex.org/I2801351115"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Min Li","raw_affiliation_strings":["Domain Group, Sydney, New South Wales, Australia"],"raw_orcid":"https://orcid.org/0009-0002-5085-9289","affiliations":[{"raw_affiliation_string":"Domain Group, Sydney, New South Wales, Australia","institution_ids":["https://openalex.org/I1329868518"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137222926","display_name":"Pooyan Asgari","orcid":"https://orcid.org/0009-0002-4873-6562"},"institutions":[{"id":"https://openalex.org/I1329868518","display_name":"NSW Department of Education","ror":"https://ror.org/05nne8c43","country_code":"AU","type":"funder","lineage":["https://openalex.org/I1329868518","https://openalex.org/I2801351115"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Pooyan Asgari","raw_affiliation_strings":["Domain Group, Sydney, New South Wales, Australia"],"raw_orcid":"https://orcid.org/0009-0002-4873-6562","affiliations":[{"raw_affiliation_string":"Domain Group, Sydney, New South Wales, Australia","institution_ids":["https://openalex.org/I1329868518"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5137261669","display_name":"Yuankai Qi","orcid":"https://orcid.org/0000-0003-4312-5682"},"institutions":[{"id":"https://openalex.org/I99043593","display_name":"Macquarie University","ror":"https://ror.org/01sf06y89","country_code":"AU","type":"education","lineage":["https://openalex.org/I99043593"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Yuankai Qi","raw_affiliation_strings":["Macquarie University, Sydney, New South Wales, Australia"],"raw_orcid":"https://orcid.org/0000-0003-4312-5682","affiliations":[{"raw_affiliation_string":"Macquarie University, Sydney, New South Wales, Australia","institution_ids":["https://openalex.org/I99043593"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"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":"1089","last_page":"1093"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.15629999339580536,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.15629999339580536,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.10750000178813934,"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":0.06369999796152115,"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/imputation","display_name":"Imputation (statistics)","score":0.45559999346733093},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3440000116825104},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.2296999990940094},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.22599999606609344}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6107000112533569},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6011999845504761},{"id":"https://openalex.org/C58041806","wikidata":"https://www.wikidata.org/wiki/Q1660484","display_name":"Imputation (statistics)","level":3,"score":0.45559999346733093},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.35269999504089355},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3440000116825104},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.29010000824928284},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.2676999866962433},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.2296999990940094},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.22599999606609344},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.21459999680519104}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3774905.3795446","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3774905.3795446","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Companion Proceedings of the ACM Web Conference 2026","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3774905.3795446","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3774905.3795446","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Companion Proceedings of the ACM Web Conference 2026","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":7,"referenced_works":["https://openalex.org/W2341645695","https://openalex.org/W2754880706","https://openalex.org/W2785362611","https://openalex.org/W3174697924","https://openalex.org/W3177318507","https://openalex.org/W3199003182","https://openalex.org/W4378942662"],"related_works":[],"abstract_inverted_index":{"Time-series":[0],"data":[1,149],"are":[2,37,63],"crucial":[3],"for":[4,29,142],"decision-making":[5],"across":[6],"domains":[7],"such":[8],"as":[9,59],"healthcare,":[10],"energy,":[11],"and":[12,44,71,89,106,113],"sensor":[13],"networks.":[14],"However,":[15],"missing":[16,79,111,134],"values":[17],"can":[18],"harm":[19],"model":[20,75],"reliability,":[21],"making":[22],"the":[23,78,102],"handling":[24],"of":[25,128],"incomplete":[26],"records":[27],"critical":[28],"robust":[30],"analytics.":[31],"Despite":[32],"recent":[33],"advances,":[34],"state-of-the-art":[35,120],"imputers":[36],"often":[38],"computationally":[39],"demanding,":[40],"require":[41],"extensive":[42],"training,":[43],"may":[45],"disrupt":[46],"temporal":[47,84],"order.":[48],"We":[49,98],"introduce":[50],"ImVision,":[51],"a":[52,72,139],"cross-modal":[53],"framework":[54],"that":[55,117],"reframes":[56],"time-series":[57,148],"imputation":[58],"image":[60],"inpainting.":[61],"Sequences":[62],"converted":[64],"into":[65],"Gramian":[66],"Angular":[67],"Field":[68],"(GAF)":[69],"heatmaps,":[70],"pretrained":[73,91],"vision":[74,144],"fills":[76],"in":[77,122,150],"pixels.":[80],"The":[81],"pipeline":[82],"maps":[83],"gaps":[85],"to":[86,146],"spatial":[87],"masks":[88],"leverages":[90],"inpainting":[92],"backbones":[93],"with":[94],"minimal":[95],"task-specific":[96],"training.":[97],"evaluate":[99],"ImVision":[100,118,136],"on":[101,125],"Beijing":[103],"Air":[104],"Quality":[105],"ETT-h1":[107],"datasets":[108],"under":[109],"various":[110],"rates":[112],"patterns.":[114],"Experiments":[115],"show":[116],"outperforms":[119],"models":[121,145],"standardized":[123],"MAE":[124],"8":[126],"out":[127],"10":[129],"settings,":[130],"especially":[131],"at":[132],"high":[133],"rates.":[135],"thus":[137],"offers":[138],"resource-efficient":[140],"strategy":[141],"reusing":[143],"impute":[147],"real-world":[151],"scenarios.":[152]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2026-05-29T00:00:00"}