{"id":"https://openalex.org/W4415538203","doi":"https://doi.org/10.1145/3746027.3754896","title":"Adaptive Prompt Learning for Blind Image Quality Assessment with Multi-modal Mixed-datasets Training","display_name":"Adaptive Prompt Learning for Blind Image Quality Assessment with Multi-modal Mixed-datasets Training","publication_year":2025,"publication_date":"2025-10-25","ids":{"openalex":"https://openalex.org/W4415538203","doi":"https://doi.org/10.1145/3746027.3754896"},"language":null,"primary_location":{"id":"doi:10.1145/3746027.3754896","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3746027.3754896","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 33rd ACM International Conference on Multimedia","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/A5101454332","display_name":"Yan Zhong","orcid":"https://orcid.org/0000-0003-0005-2620"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yan Zhong","raw_affiliation_strings":["School of Mathematical Sciences, State Key Laboratory of Multimedia Information Processing, School of Computer Science, Peking University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0003-0005-2620","affiliations":[{"raw_affiliation_string":"School of Mathematical Sciences, State Key Laboratory of Multimedia Information Processing, School of Computer Science, Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5082323642","display_name":"Xinping Zhao","orcid":"https://orcid.org/0000-0001-6387-1442"},"institutions":[{"id":"https://openalex.org/I158809036","display_name":"Shenzhen Institute of Information Technology","ror":"https://ror.org/03wrf9427","country_code":"CN","type":"education","lineage":["https://openalex.org/I158809036"]},{"id":"https://openalex.org/I204983213","display_name":"Harbin Institute of Technology","ror":"https://ror.org/01yqg2h08","country_code":"CN","type":"education","lineage":["https://openalex.org/I204983213"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xinping Zhao","raw_affiliation_strings":["School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen, China"],"raw_orcid":"https://orcid.org/0000-0001-6387-1442","affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen, China","institution_ids":["https://openalex.org/I158809036","https://openalex.org/I204983213"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5011780655","display_name":"Li Zhang","orcid":"https://orcid.org/0000-0003-1610-6056"},"institutions":[{"id":"https://openalex.org/I126520041","display_name":"University of Science and Technology of China","ror":"https://ror.org/04c4dkn09","country_code":"CN","type":"education","lineage":["https://openalex.org/I126520041","https://openalex.org/I19820366"]},{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I2802624667","display_name":"Hefei Institutes of Physical Science","ror":"https://ror.org/046n57345","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I2802624667"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Li Zhang","raw_affiliation_strings":["Hefei Institute of Physical Science, Chinese Academy of Sciences, University of Science and Technology of China, Hefei, China"],"raw_orcid":"https://orcid.org/0000-0003-1610-6056","affiliations":[{"raw_affiliation_string":"Hefei Institute of Physical Science, Chinese Academy of Sciences, University of Science and Technology of China, Hefei, China","institution_ids":["https://openalex.org/I126520041","https://openalex.org/I19820366","https://openalex.org/I2802624667"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5085373820","display_name":"Xinyuan Song","orcid":"https://orcid.org/0009-0005-4209-5671"},"institutions":[{"id":"https://openalex.org/I189196454","display_name":"The University of Texas at Arlington","ror":"https://ror.org/019kgqr73","country_code":"US","type":"education","lineage":["https://openalex.org/I189196454"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xinyuan Song","raw_affiliation_strings":["Department of Computer Science and Engineering, University of Texas at Arlington, Arlington, Texas, USA"],"raw_orcid":"https://orcid.org/0009-0005-4209-5671","affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, University of Texas at Arlington, Arlington, Texas, USA","institution_ids":["https://openalex.org/I189196454"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101606698","display_name":"Tingting Jiang","orcid":"https://orcid.org/0000-0002-5372-0656"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tingting Jiang","raw_affiliation_strings":["National Engineering Research Center of Visual Technology, State Key Laboratory of Multimedia Information Processing, School of Computer Science, National Biomedical Imaging Center, Peking University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-5372-0656","affiliations":[{"raw_affiliation_string":"National Engineering Research Center of Visual Technology, State Key Laboratory of Multimedia Information Processing, School of Computer Science, National Biomedical Imaging Center, Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":7,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.32713668,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"7453","last_page":"7462"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11165","display_name":"Image and Video Quality Assessment","score":1.0,"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/T11165","display_name":"Image and Video Quality Assessment","score":1.0,"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/T11105","display_name":"Advanced Image Processing Techniques","score":0.9950000047683716,"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/T11659","display_name":"Advanced Image Fusion Techniques","score":0.9944000244140625,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"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/generalization","display_name":"Generalization","score":0.7278000116348267},{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.7081999778747559},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.6682999730110168},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5512999892234802},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.475600004196167},{"id":"https://openalex.org/keywords/image-quality","display_name":"Image quality","score":0.39879998564720154},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.37220001220703125},{"id":"https://openalex.org/keywords/joint","display_name":"Joint (building)","score":0.36579999327659607}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7621999979019165},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.7278000116348267},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.7081999778747559},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.6682999730110168},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6373000144958496},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5512999892234802},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5187000036239624},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.475600004196167},{"id":"https://openalex.org/C55020928","wikidata":"https://www.wikidata.org/wiki/Q3813865","display_name":"Image quality","level":3,"score":0.39879998564720154},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.37220001220703125},{"id":"https://openalex.org/C18555067","wikidata":"https://www.wikidata.org/wiki/Q8375051","display_name":"Joint (building)","level":2,"score":0.36579999327659607},{"id":"https://openalex.org/C2779346075","wikidata":"https://www.wikidata.org/wiki/Q7268763","display_name":"Quality Score","level":3,"score":0.35409998893737793},{"id":"https://openalex.org/C22367795","wikidata":"https://www.wikidata.org/wiki/Q7625208","display_name":"Structured prediction","level":2,"score":0.34779998660087585},{"id":"https://openalex.org/C2780980858","wikidata":"https://www.wikidata.org/wiki/Q110022","display_name":"Dual (grammatical number)","level":2,"score":0.33730000257492065},{"id":"https://openalex.org/C197654239","wikidata":"https://www.wikidata.org/wiki/Q7430757","display_name":"Scene statistics","level":3,"score":0.32519999146461487},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3125999867916107},{"id":"https://openalex.org/C2776036281","wikidata":"https://www.wikidata.org/wiki/Q48769818","display_name":"Constraint (computer-aided design)","level":2,"score":0.28850001096725464},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.28209999203681946},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.26010000705718994},{"id":"https://openalex.org/C149364088","wikidata":"https://www.wikidata.org/wiki/Q185917","display_name":"Translation (biology)","level":4,"score":0.2583000063896179},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.257999986410141},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2556999921798706},{"id":"https://openalex.org/C183322885","wikidata":"https://www.wikidata.org/wiki/Q17007702","display_name":"Context model","level":3,"score":0.25429999828338623}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3746027.3754896","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3746027.3754896","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 33rd ACM International Conference on Multimedia","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":27,"referenced_works":["https://openalex.org/W1974013408","https://openalex.org/W1977725648","https://openalex.org/W1979451680","https://openalex.org/W2138790992","https://openalex.org/W2148848374","https://openalex.org/W2161907179","https://openalex.org/W2563786098","https://openalex.org/W2953590133","https://openalex.org/W2963430933","https://openalex.org/W3002992380","https://openalex.org/W3022710784","https://openalex.org/W3030380536","https://openalex.org/W3035595647","https://openalex.org/W3035712445","https://openalex.org/W3035719652","https://openalex.org/W3047011367","https://openalex.org/W3105164497","https://openalex.org/W3109016160","https://openalex.org/W3135955502","https://openalex.org/W3210514413","https://openalex.org/W4214745154","https://openalex.org/W4221053795","https://openalex.org/W4224267514","https://openalex.org/W4312310776","https://openalex.org/W4367146870","https://openalex.org/W4386076169","https://openalex.org/W4390189747"],"related_works":[],"abstract_inverted_index":{"Due":[0],"to":[1,32,56,64,85,92,122,199],"the":[2,69,94,125,150,159,202,213,219],"high":[3],"cost":[4],"and":[5,132,183,208],"small":[6],"scale":[7],"of":[8,68,96,222],"Image":[9],"Quality":[10],"Assessment":[11],"(IQA)":[12],"datasets,":[13],"achieving":[14],"robust":[15],"generalization":[16,95],"remains":[17],"challenging":[18],"for":[19,76,89,167],"prevalent":[20],"Blind":[21],"IQA":[22],"(BIQA)":[23],"methods.":[24],"Traditional":[25],"deep":[26],"learning-based":[27],"methods":[28],"emphasize":[29],"visual":[30,131],"information":[31,140],"capture":[33,138],"quality":[34,78,139,151,177,181,187],"features,":[35,134],"while":[36],"recent":[37],"developments":[38],"in":[39,46],"Vision-Language":[40],"Models":[41],"(VLMs)":[42],"demonstrate":[43],"strong":[44],"potential":[45],"learning":[47],"generalizable":[48],"representations":[49],"through":[50],"textual":[51,133],"information.":[52,71,127],"However,":[53],"applying":[54],"VLMs":[55],"BIQA":[57,98,107],"poses":[58],"three":[59],"major":[60],"Challenges:":[61],"(1)":[62],"How":[63,84],"make":[65],"full":[66],"use":[67,86],"multi-modal":[70],"(2)":[72],"The":[73],"prompt":[74,110],"engineering":[75],"appropriate":[77],"description":[79],"is":[80],"extremely":[81],"time-consuming.":[82],"(3)":[83],"mixed":[87],"data":[88],"joint":[90],"training":[91,160],"enhance":[93],"VLM-based":[97],"model.":[99],"To":[100],"this":[101],"end,":[102],"we":[103,116,148,172,191],"propose":[104,117,192],"a":[105,118,142,175,179,184,193],"Multi-modal":[106],"method":[108],"with":[109,141,155],"learning,":[111],"named":[112],"MMP-IQA.":[113,223],"For":[114,146,170],"(1),":[115],"conditional":[119],"fusion":[120],"module":[121],"better":[123],"utilize":[124],"cross-modality":[126],"By":[128],"jointly":[129,173],"adjusting":[130],"our":[135],"model":[136,149],"can":[137,163],"stronger":[143],"representation":[144],"ability.":[145],"(2),":[147],"prompt's":[152],"context":[153],"words":[154],"learnable":[156],"vectors":[157],"during":[158],"process,":[161],"which":[162],"be":[164],"adaptively":[165,200],"updated":[166],"superior":[168,220],"performances.":[169],"(3),":[171],"train":[174],"linearity-induced":[176],"evaluator,":[178,182],"relative":[180],"dataset-specific":[185],"absolute":[186],"evaluator.":[188],"In":[189],"addition,":[190],"dual":[194],"automatic":[195],"weight":[196],"adjustment":[197],"strategy":[198],"balance":[201],"loss":[203],"weights":[204],"between":[205],"different":[206],"datasets":[207],"among":[209],"various":[210],"losses":[211],"within":[212],"same":[214],"dataset.":[215],"Extensive":[216],"experiments":[217],"illustrate":[218],"effectiveness":[221]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-25T00:00:00"}
