{"id":"https://openalex.org/W4408352856","doi":"https://doi.org/10.1109/icassp49660.2025.10889316","title":"Self-Supervised Learning and Image-Prompt Fusion for AIGC Image Quality Assessment","display_name":"Self-Supervised Learning and Image-Prompt Fusion for AIGC Image Quality Assessment","publication_year":2025,"publication_date":"2025-03-12","ids":{"openalex":"https://openalex.org/W4408352856","doi":"https://doi.org/10.1109/icassp49660.2025.10889316"},"language":"en","primary_location":{"id":"doi:10.1109/icassp49660.2025.10889316","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp49660.2025.10889316","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"},"type":"article","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/A5101408846","display_name":"Yan Zhao","orcid":"https://orcid.org/0000-0002-7980-6925"},"institutions":[{"id":"https://openalex.org/I111599522","display_name":"Jiangnan University","ror":"https://ror.org/04mkzax54","country_code":"CN","type":"education","lineage":["https://openalex.org/I111599522"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yan Zhao","raw_affiliation_strings":["Jiangnan University,The School of Artificial Intelligence and Computer Science,Wuxi,China,214122"],"affiliations":[{"raw_affiliation_string":"Jiangnan University,The School of Artificial Intelligence and Computer Science,Wuxi,China,214122","institution_ids":["https://openalex.org/I111599522"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5040344946","display_name":"Qingbing Sang","orcid":"https://orcid.org/0000-0002-9883-792X"},"institutions":[{"id":"https://openalex.org/I111599522","display_name":"Jiangnan University","ror":"https://ror.org/04mkzax54","country_code":"CN","type":"education","lineage":["https://openalex.org/I111599522"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qingbing Sang","raw_affiliation_strings":["Jiangnan University,The School of Artificial Intelligence and Computer Science,Wuxi,China,214122"],"affiliations":[{"raw_affiliation_string":"Jiangnan University,The School of Artificial Intelligence and Computer Science,Wuxi,China,214122","institution_ids":["https://openalex.org/I111599522"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5114151545","display_name":"Zhaohong Deng","orcid":"https://orcid.org/0000-0002-0790-6492"},"institutions":[{"id":"https://openalex.org/I111599522","display_name":"Jiangnan University","ror":"https://ror.org/04mkzax54","country_code":"CN","type":"education","lineage":["https://openalex.org/I111599522"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhaohong Deng","raw_affiliation_strings":["Jiangnan University,The School of Artificial Intelligence and Computer Science,Wuxi,China,214122"],"affiliations":[{"raw_affiliation_string":"Jiangnan University,The School of Artificial Intelligence and Computer Science,Wuxi,China,214122","institution_ids":["https://openalex.org/I111599522"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5112052641","display_name":"Xiaojun Wu","orcid":"https://orcid.org/0009-0000-0199-5001"},"institutions":[{"id":"https://openalex.org/I111599522","display_name":"Jiangnan University","ror":"https://ror.org/04mkzax54","country_code":"CN","type":"education","lineage":["https://openalex.org/I111599522"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaojun Wu","raw_affiliation_strings":["Jiangnan University,The School of Artificial Intelligence and Computer Science,Wuxi,China,214122"],"affiliations":[{"raw_affiliation_string":"Jiangnan University,The School of Artificial Intelligence and Computer Science,Wuxi,China,214122","institution_ids":["https://openalex.org/I111599522"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5101408846"],"corresponding_institution_ids":["https://openalex.org/I111599522"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.07193855,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11659","display_name":"Advanced Image Fusion Techniques","score":0.9569000005722046,"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"}},"topics":[{"id":"https://openalex.org/T11659","display_name":"Advanced Image Fusion Techniques","score":0.9569000005722046,"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/computer-science","display_name":"Computer science","score":0.7232344150543213},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.689146101474762},{"id":"https://openalex.org/keywords/image-fusion","display_name":"Image fusion","score":0.6438050866127014},{"id":"https://openalex.org/keywords/image-quality","display_name":"Image quality","score":0.5806035399436951},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.5360071659088135},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5181271433830261},{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.4918785095214844},{"id":"https://openalex.org/keywords/fusion","display_name":"Fusion","score":0.4890623986721039},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.37328261137008667}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7232344150543213},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.689146101474762},{"id":"https://openalex.org/C69744172","wikidata":"https://www.wikidata.org/wiki/Q860822","display_name":"Image fusion","level":3,"score":0.6438050866127014},{"id":"https://openalex.org/C55020928","wikidata":"https://www.wikidata.org/wiki/Q3813865","display_name":"Image quality","level":3,"score":0.5806035399436951},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.5360071659088135},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5181271433830261},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.4918785095214844},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.4890623986721039},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.37328261137008667},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp49660.2025.10889316","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp49660.2025.10889316","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","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/W1982471090","https://openalex.org/W2051596736","https://openalex.org/W2102166818","https://openalex.org/W2172058006","https://openalex.org/W2765811365","https://openalex.org/W2905544033","https://openalex.org/W3035719652","https://openalex.org/W3194280054","https://openalex.org/W4200635121","https://openalex.org/W4224267514","https://openalex.org/W4312933868","https://openalex.org/W4386071753","https://openalex.org/W4386211581","https://openalex.org/W4387010079","https://openalex.org/W4391490252","https://openalex.org/W4400490152","https://openalex.org/W6784333009","https://openalex.org/W6791353385","https://openalex.org/W6796242362","https://openalex.org/W6809885388","https://openalex.org/W6859252943"],"related_works":["https://openalex.org/W2788731446","https://openalex.org/W2204403038","https://openalex.org/W3152170969","https://openalex.org/W2379054866","https://openalex.org/W2370195708","https://openalex.org/W1490651872","https://openalex.org/W2139242969","https://openalex.org/W2284201331","https://openalex.org/W2095903272","https://openalex.org/W1989561795"],"abstract_inverted_index":{"With":[0],"the":[1,7,26,45,64,74,102,107,116,123,130,164,200],"rapid":[2],"advancement":[3],"of":[4,9,66,76,109,125,133,168],"artificial":[5],"intelligence,":[6],"field":[8],"Artificial":[10],"Intelligence":[11],"Generated":[12],"Content":[13],"(AIGC)":[14],"has":[15,31],"seen":[16],"significant":[17],"growth.":[18],"As":[19],"AI-generated":[20],"images":[21,50],"(AIGIs)":[22],"become":[23],"increasingly":[24],"prevalent,":[25],"AIGC":[27],"image":[28,37,93,120,157,170,185],"quality":[29,38,121],"assessment(AIGCIQA)":[30],"gained":[32],"critical":[33],"importance.":[34],"However,":[35],"traditional":[36],"assessment":[39],"methods":[40],"struggle":[41],"to":[42,57,129,173],"account":[43],"for":[44,147],"complex":[46],"relationship":[47],"between":[48,119],"generated":[49,95],"and":[51,122,158,183],"their":[52],"corresponding":[53],"text":[54,159,180],"prompts,":[55],"leading":[56],"suboptimal":[58],"performance":[59,205],"in":[60],"AIGCIQA":[61,71,209],"tasks.":[62],"Additionally,":[63,149],"scarcity":[65],"subjective":[67],"annotation":[68],"data":[69],"within":[70],"datasets":[72],"limits":[73],"effectiveness":[75],"deep":[77],"learning":[78],"models.":[79],"To":[80],"address":[81],"these":[82],"challenges,":[83],"we":[84,140,150],"propose":[85],"a":[86,137,143,152,178,188],"novel":[87],"self-supervised":[88],"task":[89],"that":[90,154,199],"utilizes":[91,177],"intermediate":[92,169],"sequences":[94,171],"during":[96],"Text-to-Image":[97],"(T2I)":[98],"model":[99,110,126,194],"generation":[100],"as":[101,112,136],"pre-training":[103],"data.":[104],"We":[105],"use":[106],"number":[108,124],"iterations":[111],"pseudo-labels":[113],"based":[114],"on":[115,206],"positive":[117],"correlation":[118],"iterations.":[127],"Due":[128],"relative":[131],"coarseness":[132],"this":[134],"pseudo-label":[135],"supervised":[138],"signal,":[139],"also":[141],"introduce":[142],"linear":[144],"interpolation":[145],"method":[146,202],"optimization.":[148],"designed":[151],"framework":[153,162],"effectively":[155],"fuses":[156],"features.":[160],"The":[161],"considers":[163],"dynamic":[165],"fading":[166],"properties":[167],"similar":[172],"video":[174],"clips.":[175],"It":[176],"specialized":[179],"prompt":[181],"template":[182],"extracts":[184],"features":[186],"through":[187],"cross-attention":[189],"mechanism,":[190],"which":[191],"significantly":[192],"improves":[193],"performance.":[195],"Experimental":[196],"results":[197],"show":[198],"proposed":[201],"achieves":[203],"state-of-the-art":[204],"three":[207],"mainstream":[208],"datasets.":[210]},"counts_by_year":[],"updated_date":"2025-12-22T23:10:17.713674","created_date":"2025-10-10T00:00:00"}