{"id":"https://openalex.org/W4414140676","doi":"https://doi.org/10.1109/icmew68306.2025.11152055","title":"Adaptive Prompt Modifier for Text-to-Image Model","display_name":"Adaptive Prompt Modifier for Text-to-Image Model","publication_year":2025,"publication_date":"2025-06-30","ids":{"openalex":"https://openalex.org/W4414140676","doi":"https://doi.org/10.1109/icmew68306.2025.11152055"},"language":"en","primary_location":{"id":"doi:10.1109/icmew68306.2025.11152055","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icmew68306.2025.11152055","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 Multimedia and Expo Workshops (ICMEW)","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/A5031545036","display_name":"Xu Ju","orcid":"https://orcid.org/0009-0005-6284-3585"},"institutions":[{"id":"https://openalex.org/I78988378","display_name":"Renmin University of China","ror":"https://ror.org/041pakw92","country_code":"CN","type":"education","lineage":["https://openalex.org/I78988378"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xinyue Ju","raw_affiliation_strings":["Renmin University of China,School of Information,Beijing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Renmin University of China,School of Information,Beijing,China","institution_ids":["https://openalex.org/I78988378"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5064368137","display_name":"Yi\u2010Peng Xu","orcid":"https://orcid.org/0000-0001-8813-2224"},"institutions":[{"id":"https://openalex.org/I78988378","display_name":"Renmin University of China","ror":"https://ror.org/041pakw92","country_code":"CN","type":"education","lineage":["https://openalex.org/I78988378"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yipeng Xu","raw_affiliation_strings":["Renmin University of China,School of Information,Beijing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Renmin University of China,School of Information,Beijing,China","institution_ids":["https://openalex.org/I78988378"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5074018705","display_name":"Gang Yang","orcid":"https://orcid.org/0000-0002-7001-2037"},"institutions":[{"id":"https://openalex.org/I78988378","display_name":"Renmin University of China","ror":"https://ror.org/041pakw92","country_code":"CN","type":"education","lineage":["https://openalex.org/I78988378"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Gang Yang","raw_affiliation_strings":["Renmin University of China,School of Information,Beijing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Renmin University of China,School of Information,Beijing,China","institution_ids":["https://openalex.org/I78988378"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I78988378"],"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":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10481","display_name":"Computer Graphics and Visualization Techniques","score":0.9959999918937683,"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"}},"topics":[{"id":"https://openalex.org/T10481","display_name":"Computer Graphics and Visualization Techniques","score":0.9959999918937683,"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"}},{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.9945999979972839,"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/T10601","display_name":"Handwritten Text Recognition Techniques","score":0.9904999732971191,"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/task","display_name":"Task (project management)","score":0.5812000036239624},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5548999905586243},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.5421000123023987},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.4706000089645386},{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.459199994802475},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.45739999413490295},{"id":"https://openalex.org/keywords/natural-language","display_name":"Natural language","score":0.3619999885559082},{"id":"https://openalex.org/keywords/supervised-learning","display_name":"Supervised learning","score":0.359499990940094},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.33820000290870667}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7328000068664551},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5950000286102295},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.5812000036239624},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5548999905586243},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.5421000123023987},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.504800021648407},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4706000089645386},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.459199994802475},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.45739999413490295},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.3619999885559082},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.359499990940094},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.33959999680519104},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.33820000290870667},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.3305000066757202},{"id":"https://openalex.org/C22367795","wikidata":"https://www.wikidata.org/wiki/Q7625208","display_name":"Structured prediction","level":2,"score":0.3061999976634979},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.29120001196861267},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.29019999504089355},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.28519999980926514},{"id":"https://openalex.org/C2776187449","wikidata":"https://www.wikidata.org/wiki/Q1513879","display_name":"Natural language generation","level":3,"score":0.27880001068115234},{"id":"https://openalex.org/C207685749","wikidata":"https://www.wikidata.org/wiki/Q2088941","display_name":"Domain knowledge","level":2,"score":0.27639999985694885},{"id":"https://openalex.org/C177284502","wikidata":"https://www.wikidata.org/wiki/Q1005390","display_name":"Adapter (computing)","level":2,"score":0.271699994802475},{"id":"https://openalex.org/C157657479","wikidata":"https://www.wikidata.org/wiki/Q2367247","display_name":"Closed captioning","level":3,"score":0.27090001106262207},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.2651999890804291},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.2606000006198883},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.25690001249313354},{"id":"https://openalex.org/C24756922","wikidata":"https://www.wikidata.org/wiki/Q1757694","display_name":"Data quality","level":3,"score":0.25220000743865967}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icmew68306.2025.11152055","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icmew68306.2025.11152055","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 Multimedia and Expo Workshops (ICMEW)","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":27,"referenced_works":["https://openalex.org/W1797268635","https://openalex.org/W2533598788","https://openalex.org/W2948978827","https://openalex.org/W2951326654","https://openalex.org/W2962770929","https://openalex.org/W2963499153","https://openalex.org/W3011199201","https://openalex.org/W3036167779","https://openalex.org/W3041956526","https://openalex.org/W3096831136","https://openalex.org/W3098267758","https://openalex.org/W3162926177","https://openalex.org/W3168867926","https://openalex.org/W4235624532","https://openalex.org/W4283388932","https://openalex.org/W4287122539","https://openalex.org/W4288089799","https://openalex.org/W4306309404","https://openalex.org/W4306705078","https://openalex.org/W4306820534","https://openalex.org/W4310428424","https://openalex.org/W4384918448","https://openalex.org/W4385571124","https://openalex.org/W4385573003","https://openalex.org/W4387293261","https://openalex.org/W4388187930","https://openalex.org/W4390872876"],"related_works":["https://openalex.org/W2772917594","https://openalex.org/W2036807459","https://openalex.org/W2058170566","https://openalex.org/W2755342338","https://openalex.org/W2166024367","https://openalex.org/W3116076068","https://openalex.org/W2229312674","https://openalex.org/W2951359407","https://openalex.org/W2079911747","https://openalex.org/W1969923398"],"abstract_inverted_index":{"With":[0],"the":[1,15,28,48,89,140,156,167,176,185,215,220,228,243,259],"increasing":[2],"prevalence":[3],"of":[4,17,32,52,92,117,170,188,245,251,261],"Large":[5,11,21],"Language":[6,12],"Models":[7,13,22],"(LLMs)":[8],"and":[9,30,76,181,203,224,257],"Multimodal":[10],"(MLLMs),":[14],"quality":[16,244,250],"data":[18,129],"generated":[19,80,133,246],"by":[20,27,81,131,134],"(LMs)":[23],"is":[24,62,281],"critically":[25],"influenced":[26],"design":[29],"formulation":[31],"prompts,":[33,38,198],"especially":[34,59],"for":[35,57,88,126,159,209],"art":[36],"generation":[37],"which":[39,248],"require":[40],"even":[41],"more":[42],"meticulous":[43],"design.":[44],"However,":[45],"due":[46],"to":[47,68,84,122,139,154,175,232,241,274],"large":[49],"search":[50],"space":[51],"natural":[53],"language,":[54],"prompt":[55,103,160,207,234,263],"engineering":[56],"LMs":[58],"Text-to-Image(T2I)":[60],"models":[61],"difficult.":[63],"While":[64],"manual":[65],"prompts":[66,79,87,132,252,275],"need":[67],"be":[69],"done":[70],"on":[71,108,111,146],"a":[72,114,201,254],"trial-and-error":[73],"adhoc":[74],"basis":[75],"lack":[77],"diversity,":[78],"LLMs":[82,112],"fail":[83],"provide":[85],"tailored":[86],"target":[90,141],"type":[91],"synthetic":[93],"data.":[94],"To":[95],"address":[96],"these":[97],"problems,":[98],"we":[99,162,218,237],"propose":[100],"an":[101],"adaptive":[102],"learning":[104],"approach,":[105],"AdaPTer,":[106],"based":[107],"supervised":[109],"fine-tuning":[110],"with":[113,150,214],"filtered":[115],"subset":[116],"task":[118,171],"model\u2019s":[119],"training":[120,168,186],"set":[121,169,208],"supply":[123],"domain":[124],"knowledge":[125],"LLMs.":[127,279],"Synthetic":[128],"guided":[130],"AdaPTer":[135,269],"have":[136],"better":[137],"adaption":[138],"domain.":[142],"This":[143],"paper":[144],"works":[145],"two":[147],"main":[148],"tasks":[149],"T2I":[151,172,189,216,230],"models.":[152,173],"Firstly,":[153],"find":[155],"correct":[157],"direction":[158],"optimization,":[161],"retrieve":[163],"image-caption":[164],"pairs":[165,183],"from":[166,253],"Due":[174],"inherent":[177],"correlation":[178],"between":[179,222],"text":[180,223],"image":[182,191],"in":[184,227,283],"process":[187],"models,":[190],"captions":[192],"naturally":[193],"emerge":[194],"as":[195],"highly":[196],"effective":[197],"subsequently":[199],"offering":[200],"vast":[202],"generally":[204],"reliable":[205],"candidate":[206],"experimentation.":[210],"By":[211],"generating":[212],"images":[213],"model,":[217],"explore":[219],"relationship":[221],"visual":[225],"information":[226],"pretrained":[229],"model":[231],"instruct":[233],"engineering.":[235],"Secondly,":[236],"use":[238],"similarity":[239],"indicator":[240],"evaluate":[242],"images,":[247],"represents":[249],"consistency":[255],"perspective,":[256],"prove":[258],"effectiveness":[260],"our":[262],"strategy.":[264],"Experimental":[265],"results":[266],"demonstrate":[267],"that":[268],"exhibits":[270],"superior":[271],"performance":[272],"compared":[273],"refined":[276],"directly":[277],"using":[278],"Code":[280],"open":[282],"https://github.com/byeolnim11/AdaPTer.git.":[284]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}