{"id":"https://openalex.org/W4415540191","doi":"https://doi.org/10.1145/3746027.3755699","title":"Gamma: Toward Generic Image Assessment with Mixture of Assessment Experts","display_name":"Gamma: Toward Generic Image Assessment with Mixture of Assessment Experts","publication_year":2025,"publication_date":"2025-10-25","ids":{"openalex":"https://openalex.org/W4415540191","doi":"https://doi.org/10.1145/3746027.3755699"},"language":null,"primary_location":{"id":"doi:10.1145/3746027.3755699","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3746027.3755699","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":"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/A5083942931","display_name":"Hantao Zhou","orcid":"https://orcid.org/0000-0002-0514-0905"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Hantao Zhou","raw_affiliation_strings":["Tsinghua University, Shenzhen, China"],"raw_orcid":"https://orcid.org/0000-0002-0514-0905","affiliations":[{"raw_affiliation_string":"Tsinghua University, Shenzhen, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Rui Yang","orcid":"https://orcid.org/0009-0006-7184-6929"},"institutions":[{"id":"https://openalex.org/I889458895","display_name":"University of Hong Kong","ror":"https://ror.org/02zhqgq86","country_code":"HK","type":"education","lineage":["https://openalex.org/I889458895"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Rui Yang","raw_affiliation_strings":["The University of Hong Kong, Hong Kong, China"],"raw_orcid":"https://orcid.org/0009-0006-7184-6929","affiliations":[{"raw_affiliation_string":"The University of Hong Kong, Hong Kong, China","institution_ids":["https://openalex.org/I889458895"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5010564305","display_name":"Longxiang Tang","orcid":"https://orcid.org/0009-0005-2704-3718"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Longxiang Tang","raw_affiliation_strings":["Tsinghua University, Shenzhen, China"],"raw_orcid":"https://orcid.org/0009-0005-2704-3718","affiliations":[{"raw_affiliation_string":"Tsinghua University, Shenzhen, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5039064197","display_name":"Guanyi Qin","orcid":"https://orcid.org/0000-0001-8414-9870"},"institutions":[{"id":"https://openalex.org/I165932596","display_name":"National University of Singapore","ror":"https://ror.org/01tgyzw49","country_code":"SG","type":"education","lineage":["https://openalex.org/I165932596"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Guanyi Qin","raw_affiliation_strings":["National University of Singapore, Singapore, Singapore"],"raw_orcid":"https://orcid.org/0000-0001-8414-9870","affiliations":[{"raw_affiliation_string":"National University of Singapore, Singapore, Singapore","institution_ids":["https://openalex.org/I165932596"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5011299161","display_name":"Runze Hu","orcid":"https://orcid.org/0000-0002-6366-3763"},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Runze Hu","raw_affiliation_strings":["Beijing Institute of Technology, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-6366-3763","affiliations":[{"raw_affiliation_string":"Beijing Institute of Technology, Beijing, China","institution_ids":["https://openalex.org/I125839683"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100754504","display_name":"Xiu Li","orcid":"https://orcid.org/0000-0003-0403-1923"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiu Li","raw_affiliation_strings":["Tsinghua University, Shenzhen, China"],"raw_orcid":"https://orcid.org/0000-0003-0403-1923","affiliations":[{"raw_affiliation_string":"Tsinghua University, Shenzhen, China","institution_ids":["https://openalex.org/I99065089"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5083942931"],"corresponding_institution_ids":["https://openalex.org/I99065089"],"apc_list":null,"apc_paid":null,"fwci":1.1332,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.83556005,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"8815","last_page":"8824"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11105","display_name":"Advanced Image Processing Techniques","score":0.9990000128746033,"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/T11105","display_name":"Advanced Image Processing Techniques","score":0.9990000128746033,"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/T10688","display_name":"Image and Signal Denoising Methods","score":0.9984999895095825,"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":0.9983999729156494,"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/quality-assessment","display_name":"Quality assessment","score":0.5741000175476074},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.4625999927520752},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.421099990606308},{"id":"https://openalex.org/keywords/cover","display_name":"Cover (algebra)","score":0.4147000014781952},{"id":"https://openalex.org/keywords/annotation","display_name":"Annotation","score":0.384799987077713},{"id":"https://openalex.org/keywords/image-quality","display_name":"Image quality","score":0.3612000048160553},{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.34850001335144043}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.703000009059906},{"id":"https://openalex.org/C3020001037","wikidata":"https://www.wikidata.org/wiki/Q836575","display_name":"Quality assessment","level":3,"score":0.5741000175476074},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5644000172615051},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4788999855518341},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.4625999927520752},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.421099990606308},{"id":"https://openalex.org/C2780428219","wikidata":"https://www.wikidata.org/wiki/Q16952335","display_name":"Cover (algebra)","level":2,"score":0.4147000014781952},{"id":"https://openalex.org/C2776321320","wikidata":"https://www.wikidata.org/wiki/Q857525","display_name":"Annotation","level":2,"score":0.384799987077713},{"id":"https://openalex.org/C55020928","wikidata":"https://www.wikidata.org/wiki/Q3813865","display_name":"Image quality","level":3,"score":0.3612000048160553},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.34850001335144043},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.3474999964237213},{"id":"https://openalex.org/C199579030","wikidata":"https://www.wikidata.org/wiki/Q2851778","display_name":"Automatic image annotation","level":4,"score":0.3361999988555908},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.32440000772476196},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.3116999864578247},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2671000063419342},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.2583000063896179},{"id":"https://openalex.org/C22367795","wikidata":"https://www.wikidata.org/wiki/Q7625208","display_name":"Structured prediction","level":2,"score":0.2547999918460846},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.2533000111579895},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.25220000743865967}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3746027.3755699","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3746027.3755699","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":[{"id":"https://openalex.org/G2935957626","display_name":null,"funder_award_id":"62192712","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":32,"referenced_works":["https://openalex.org/W1552073401","https://openalex.org/W1982471090","https://openalex.org/W2181646778","https://openalex.org/W2777280533","https://openalex.org/W2894938704","https://openalex.org/W2897228451","https://openalex.org/W2916909316","https://openalex.org/W2953590133","https://openalex.org/W2963918210","https://openalex.org/W3028365735","https://openalex.org/W3030380536","https://openalex.org/W3035719652","https://openalex.org/W3133769291","https://openalex.org/W3135971978","https://openalex.org/W3173909755","https://openalex.org/W3209753567","https://openalex.org/W4214745154","https://openalex.org/W4225466252","https://openalex.org/W4312847199","https://openalex.org/W4312933868","https://openalex.org/W4322576760","https://openalex.org/W4360584327","https://openalex.org/W4367146870","https://openalex.org/W4382450052","https://openalex.org/W4382462760","https://openalex.org/W4386075639","https://openalex.org/W4386076169","https://openalex.org/W4402754104","https://openalex.org/W4402754294","https://openalex.org/W4403792521","https://openalex.org/W4408422328","https://openalex.org/W4413157288"],"related_works":[],"abstract_inverted_index":{"Image":[0],"assessment":[1,41,56,90,175],"aims":[2],"to":[3,37,46,95,121,146],"evaluate":[4],"the":[5,153],"quality":[6],"and":[7,11,21,118,125,150,167],"aesthetics":[8],"of":[9,55,71,110],"images":[10,78],"has":[12],"been":[13],"applied":[14],"across":[15,98],"various":[16,160],"scenarios,":[17],"such":[18],"as":[19],"natural":[20],"AIGC":[22],"scenes.":[23,161,195],"Existing":[24],"methods":[25,188],"mostly":[26],"address":[27,102],"these":[28],"sub-tasks":[29],"or":[30,50],"scenes":[31,81],"individually.":[32],"While":[33],"some":[34],"works":[35],"attempt":[36],"develop":[38],"unified":[39,86,182],"image":[40,89,174],"models,":[42],"they":[43],"have":[44],"struggled":[45],"achieve":[47],"satisfactory":[48],"performance":[49],"cover":[51],"a":[52,64,108,136],"broad":[53],"spectrum":[54],"scenarios.":[57,176],"In":[58,132],"this":[59,103],"paper,":[60],"we":[61,105,134],"present":[62],"Gamma,":[63],"Generic":[65],"imAge":[66],"assessMent":[67],"model":[68,164],"using":[69],"Mixture":[70,109],"Assessment":[72,111],"Experts,":[73],"which":[74,115,142],"can":[75],"effectively":[76],"assess":[77],"from":[79],"diverse":[80],"through":[82],"mixed-dataset":[83],"training.":[84],"Achieving":[85],"training":[87],"in":[88],"presents":[91],"significant":[92,190],"challenges":[93],"due":[94],"annotation":[96],"biases":[97],"different":[99,129],"datasets.":[100],"To":[101],"issue,":[104],"first":[106],"propose":[107],"Experts":[112],"(MoAE)":[113],"module,":[114],"employs":[116],"shared":[117],"adaptive":[119],"experts":[120],"dynamically":[122],"learn":[123],"common":[124],"specific":[126],"knowledge":[127,149],"for":[128,159],"datasets,":[130],"respectively.":[131],"addition,":[133],"introduce":[135],"Scene-based":[137],"Differential":[138],"Prompt":[139],"(SDP)":[140],"strategy,":[141],"uses":[143],"scene-specific":[144],"prompts":[145],"provide":[147],"prior":[148],"guidance":[151],"during":[152],"learning":[154],"process,":[155],"further":[156],"boosting":[157],"adaptation":[158],"Our":[162],"Gamma":[163,183],"is":[165],"trained":[166],"evaluated":[168],"on":[169],"12":[170],"datasets":[171],"spanning":[172],"6":[173],"Extensive":[177],"experiments":[178],"show":[179],"that":[180],"our":[181],"outperforms":[184],"other":[185],"state-of-the-art":[186],"mixed-training":[187],"by":[189],"margins":[191],"while":[192],"covering":[193],"more":[194],"Codes":[196],"are":[197],"available":[198],"at":[199],"https://github.com/zht8506/Gamma.":[200]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-25T00:00:00"}