{"id":"https://openalex.org/W7123969972","doi":"https://doi.org/10.1109/mmsp64401.2025.11324217","title":"CompBench: Benchmarking and Comparing Image Generation with Large Multimodal Models","display_name":"CompBench: Benchmarking and Comparing Image Generation with Large Multimodal Models","publication_year":2025,"publication_date":"2025-09-21","ids":{"openalex":"https://openalex.org/W7123969972","doi":"https://doi.org/10.1109/mmsp64401.2025.11324217"},"language":null,"primary_location":{"id":"doi:10.1109/mmsp64401.2025.11324217","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mmsp64401.2025.11324217","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Workshop on Multimedia Signal Processing (MMSP)","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/A5122960303","display_name":"Jiarui Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jiarui Wang","raw_affiliation_strings":["Shanghai Jiao Tong University,Shanghai,China"],"affiliations":[{"raw_affiliation_string":"Shanghai Jiao Tong University,Shanghai,China","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015291775","display_name":"Huiyu Duan","orcid":"https://orcid.org/0000-0002-6519-4067"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Huiyu Duan","raw_affiliation_strings":["Shanghai Jiao Tong University,Shanghai,China"],"affiliations":[{"raw_affiliation_string":"Shanghai Jiao Tong University,Shanghai,China","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5113402572","display_name":"Yuke Xing","orcid":"https://orcid.org/0000-0002-7165-1615"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuke Xing","raw_affiliation_strings":["Shanghai Jiao Tong University,Shanghai,China"],"affiliations":[{"raw_affiliation_string":"Shanghai Jiao Tong University,Shanghai,China","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017559302","display_name":"Yiling Xu","orcid":"https://orcid.org/0009-0000-8622-2066"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yiling Xu","raw_affiliation_strings":["Shanghai Jiao Tong University,Shanghai,China"],"affiliations":[{"raw_affiliation_string":"Shanghai Jiao Tong University,Shanghai,China","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5122986640","display_name":"Guangtao Zhai","orcid":null},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guangtao Zhai","raw_affiliation_strings":["Shanghai Jiao Tong University,Shanghai,China"],"affiliations":[{"raw_affiliation_string":"Shanghai Jiao Tong University,Shanghai,China","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5122911245","display_name":"Xiongkuo Min","orcid":null},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiongkuo Min","raw_affiliation_strings":["Shanghai Jiao Tong University,Shanghai,China"],"affiliations":[{"raw_affiliation_string":"Shanghai Jiao Tong University,Shanghai,China","institution_ids":["https://openalex.org/I183067930"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5122960303"],"corresponding_institution_ids":["https://openalex.org/I183067930"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.66494348,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"54","last_page":"59"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11714","display_name":"Multimodal Machine Learning Applications","score":0.5435000061988831,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.5435000061988831,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.3504999876022339,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.007400000002235174,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/benchmarking","display_name":"Benchmarking","score":0.9222999811172485},{"id":"https://openalex.org/keywords/pairwise-comparison","display_name":"Pairwise comparison","score":0.9089000225067139},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.763700008392334},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.7548999786376953},{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.47929999232292175},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.45579999685287476},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.444599986076355}],"concepts":[{"id":"https://openalex.org/C86251818","wikidata":"https://www.wikidata.org/wiki/Q816754","display_name":"Benchmarking","level":2,"score":0.9222999811172485},{"id":"https://openalex.org/C184898388","wikidata":"https://www.wikidata.org/wiki/Q1435712","display_name":"Pairwise comparison","level":2,"score":0.9089000225067139},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.763700008392334},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.7548999786376953},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7063999772071838},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6586999893188477},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.48240000009536743},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.47929999232292175},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4587000012397766},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.45579999685287476},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.444599986076355},{"id":"https://openalex.org/C26760741","wikidata":"https://www.wikidata.org/wiki/Q160402","display_name":"Perception","level":2,"score":0.3905999958515167},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.3700000047683716},{"id":"https://openalex.org/C55020928","wikidata":"https://www.wikidata.org/wiki/Q3813865","display_name":"Image quality","level":3,"score":0.34769999980926514},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.29089999198913574},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.2809000015258789},{"id":"https://openalex.org/C16345878","wikidata":"https://www.wikidata.org/wiki/Q107472979","display_name":"Orientation (vector space)","level":2,"score":0.27059999108314514},{"id":"https://openalex.org/C3020001037","wikidata":"https://www.wikidata.org/wiki/Q836575","display_name":"Quality assessment","level":3,"score":0.25619998574256897}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/mmsp64401.2025.11324217","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mmsp64401.2025.11324217","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Workshop on Multimedia Signal Processing (MMSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320321543","display_name":"China Postdoctoral Science Foundation","ror":"https://ror.org/0426zh255"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":42,"referenced_works":["https://openalex.org/W1982471090","https://openalex.org/W2051596736","https://openalex.org/W2102166818","https://openalex.org/W2162915697","https://openalex.org/W2472413205","https://openalex.org/W2473697052","https://openalex.org/W2560703252","https://openalex.org/W2571248288","https://openalex.org/W2622486119","https://openalex.org/W2777280533","https://openalex.org/W2794680924","https://openalex.org/W2905544033","https://openalex.org/W3035719652","https://openalex.org/W3153469116","https://openalex.org/W4308772371","https://openalex.org/W4312933868","https://openalex.org/W4367146870","https://openalex.org/W4382462760","https://openalex.org/W4386076169","https://openalex.org/W4386211581","https://openalex.org/W4387010079","https://openalex.org/W4390874002","https://openalex.org/W4391490252","https://openalex.org/W4402727764","https://openalex.org/W4402753874","https://openalex.org/W4402916399","https://openalex.org/W4402916469","https://openalex.org/W4402996555","https://openalex.org/W4403844348","https://openalex.org/W4403938629","https://openalex.org/W4413145497","https://openalex.org/W4413145894","https://openalex.org/W4413147296","https://openalex.org/W4413158015","https://openalex.org/W4413158163","https://openalex.org/W4415025762","https://openalex.org/W4415539098","https://openalex.org/W4415539611","https://openalex.org/W4415708541","https://openalex.org/W4415797571","https://openalex.org/W4415797934","https://openalex.org/W7123335097"],"related_works":[],"abstract_inverted_index":{"Recent":[0],"advancements":[1],"in":[2,21],"large":[3],"multimodal":[4],"models":[5,93,131],"(LMMs)":[6],"have":[7],"significantly":[8],"enhanced":[9],"both":[10,128,158],"text-to-image":[11],"(T2I)":[12],"generation":[13,130],"and":[14,24,39,53,69,113,121,125,132,161,181],"image-to-text":[15],"(I2T)":[16],"interpretation.":[17],"However,":[18],"critical":[19],"challenges":[20],"perceptual":[22,112],"quality":[23,54,102,149],"text-image":[25,114],"correspondence":[26,115],"remain":[27],"hindering":[28],"the":[29,51,64,76,178],"practicality":[30],"of":[31,55,177],"AI-generated":[32],"images":[33],"(AIGIs).":[34],"Therefore,":[35],"a":[36,142],"reliable":[37],"benchmark":[38],"automatic":[40],"model":[41,162],"for":[42,67,127,154],"AIGI":[43,78,92,133],"evaluation":[44,144],"is":[45],"desirable,":[46],"which":[47,73],"heavily":[48],"relies":[49],"on":[50,137],"scale":[52],"human":[56,97,174],"annotations.":[57],"To":[58],"this":[59],"end,":[60],"we":[61,139],"present":[62],"CompBench,":[63,138],"largest":[65,77],"dataset":[66,180],"benchmarking":[68,124],"comparing":[70],"image":[71,86],"generation,":[72],"features:":[74],"(i)":[75],"pair":[79],"comparison":[80,110,134,156],"dataset,":[81],"comprising":[82],"616,346":[83],"carefully":[84],"curated":[85],"pairs":[87],"generated":[88],"by":[89],"24":[90],"state-of-the-art":[91,169],"annotated":[94],"with":[95],"1.6M+":[96],"annotations,":[98],"enabling":[99],"robust":[100],"relative":[101],"assessment":[103],"through":[104],"pairwise":[105,109,155],"comparison,":[106],"(ii)":[107],"multi-dimensional":[108],"from":[111,151],"perspectives":[116],"across":[117],"three":[118],"difficulty":[119],"levels,":[120],"(iii)":[122],"bidirectional":[123],"evaluating":[126],"T2I":[129],"models.":[135],"Based":[136],"propose":[140],"LMM4Comp,":[141],"LMM-based":[143],"metric":[145,183],"that":[146,166],"learns":[147],"nuanced":[148],"distinctions":[150],"multiple":[152],"dimensions":[153],"at":[157,187],"instance":[159],"level":[160],"level.":[163],"Experiments":[164],"demonstrate":[165],"LMM4Comp":[167,182],"achieves":[168],"performance,":[170],"highly":[171],"aligning":[172],"to":[173],"preference.":[175],"Both":[176],"CompBench":[179],"will":[184],"be":[185],"released":[186],"https://github.com/IntMeGroup/CompBench.":[188]},"counts_by_year":[],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2026-01-14T00:00:00"}