{"id":"https://openalex.org/W7133953168","doi":"https://doi.org/10.48550/arxiv.2603.03907","title":"Fine-grained Image Aesthetic Assessment: Learning Discriminative Scores from Relative Ranks","display_name":"Fine-grained Image Aesthetic Assessment: Learning Discriminative Scores from Relative Ranks","publication_year":2026,"publication_date":"2026-03-04","ids":{"openalex":"https://openalex.org/W7133953168","doi":"https://doi.org/10.48550/arxiv.2603.03907"},"language":null,"primary_location":{"id":"pmh:doi:10.48550/arxiv.2603.03907","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"publisher-specific-oa","license_id":"https://openalex.org/licenses/publisher-specific-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"type":"article","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":null,"any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5128161753","display_name":"Zhichao Yang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Zhichao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128169806","display_name":"Jianjie Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Jianjie","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128188281","display_name":"Zhixianhe Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Zhixianhe","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128198743","display_name":"Pangu Xie","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xie, Pangu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044278717","display_name":"Xiangfei Sheng","orcid":"https://orcid.org/0009-0004-8468-1970"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sheng, Xiangfei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128206026","display_name":"Pengfei Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Pengfei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5128144568","display_name":"Leida Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Leida","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"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":0.25945626,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11605","display_name":"Visual Attention and Saliency Detection","score":0.988099992275238,"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/T11605","display_name":"Visual Attention and Saliency Detection","score":0.988099992275238,"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/T12650","display_name":"Aesthetic Perception and Analysis","score":0.004600000102072954,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T11714","display_name":"Multimodal Machine Learning Applications","score":0.0008999999845400453,"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/discriminative-model","display_name":"Discriminative model","score":0.798799991607666},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.5408999919891357},{"id":"https://openalex.org/keywords/pairwise-comparison","display_name":"Pairwise comparison","score":0.506600022315979},{"id":"https://openalex.org/keywords/rank","display_name":"Rank (graph theory)","score":0.4708000123500824},{"id":"https://openalex.org/keywords/calibration","display_name":"Calibration","score":0.40310001373291016},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.39570000767707825},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.3758000135421753}],"concepts":[{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.798799991607666},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6833000183105469},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6509000062942505},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.5408999919891357},{"id":"https://openalex.org/C184898388","wikidata":"https://www.wikidata.org/wiki/Q1435712","display_name":"Pairwise comparison","level":2,"score":0.506600022315979},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.4708000123500824},{"id":"https://openalex.org/C165838908","wikidata":"https://www.wikidata.org/wiki/Q736777","display_name":"Calibration","level":2,"score":0.40310001373291016},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.39570000767707825},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.38830000162124634},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.3758000135421753},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.3296999931335449},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.30820000171661377},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.30809998512268066},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.2962999939918518},{"id":"https://openalex.org/C75165309","wikidata":"https://www.wikidata.org/wiki/Q2258979","display_name":"Search engine indexing","level":2,"score":0.29440000653266907},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.2858999967575073},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.2858000099658966},{"id":"https://openalex.org/C3018304881","wikidata":"https://www.wikidata.org/wiki/Q1435712","display_name":"Paired comparison","level":2,"score":0.2766999900341034},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.2662000060081482},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.26440000534057617},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.25369998812675476}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:doi:10.48550/arxiv.2603.03907","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"publisher-specific-oa","license_id":"https://openalex.org/licenses/publisher-specific-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},{"id":"doi:10.48550/arxiv.2603.03907","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.03907","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:doi:10.48550/arxiv.2603.03907","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"publisher-specific-oa","license_id":"https://openalex.org/licenses/publisher-specific-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","score":0.73525470495224,"display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Image":[0],"aesthetic":[1,38,62,79,149,169],"assessment":[2,170],"(IAA)":[3],"has":[4],"extensive":[5],"applications":[6],"in":[7,76,171,179],"content":[8],"creation,":[9],"album":[10],"management,":[11],"and":[12,108,124,133,162,184],"recommendation":[13],"systems,":[14],"etc.":[15],"In":[16],"such":[17],"applications,":[18],"it":[19],"is":[20],"commonly":[21],"needed":[22],"to":[23,44,127],"pick":[24],"out":[25],"the":[26,83,129,187,190],"most":[27],"aesthetically":[28],"pleasing":[29],"image":[30],"from":[31,102,151],"a":[32,40,88,142],"series":[33],"of":[34,131,189],"images":[35,59,94],"with":[36,60,92],"subtle":[37],"variations,":[39],"topic":[41],"we":[42,85,138],"refer":[43],"as":[45],"fine-grained":[46,78,89,172],"IAA.":[47],"Unfortunately,":[48],"state-of-the-art":[49],"IAA":[50,90,144],"models":[51,72],"are":[52,64,73,100,111],"typically":[53],"designed":[54],"for":[55],"coarse-grained":[56,180],"evaluation,":[57],"where":[58],"notable":[61],"differences":[63],"evaluated":[65],"independently":[66],"on":[67,136],"an":[68],"absolute":[69],"scale.":[70],"These":[71],"inherently":[74],"limited":[75],"discriminating":[77],"differences.":[80],"To":[81],"address":[82],"dilemma,":[84],"contribute":[86],"FGAesthetics,":[87,137],"database":[91],"32,217":[93],"organized":[95],"into":[96],"10,028":[97],"series,":[98],"which":[99],"sourced":[101],"diverse":[103],"categories":[104],"including":[105],"Natural,":[106],"AIGC,":[107],"Cropping.":[109],"Annotations":[110],"collected":[112],"via":[113],"pairwise":[114],"comparisons":[115,185],"within":[116],"each":[117],"series.":[118],"We":[119],"also":[120],"devise":[121],"Series":[122],"Refinement":[123],"Rank":[125],"Calibration":[126],"ensure":[128],"reliability":[130],"data":[132],"labels.":[134],"Based":[135],"further":[139],"propose":[140],"FGAesQ,":[141],"novel":[143],"framework":[145],"that":[146],"learns":[147],"discriminative":[148],"scores":[150],"relative":[152],"ranks":[153],"through":[154],"Difference-preserved":[155],"Tokenization":[156],"(DiffToken),":[157],"Comparative":[158],"Text-assisted":[159],"Alignment":[160],"(CTAlign),":[161],"Rank-aware":[163],"Regression":[164],"(RankReg).":[165],"FGAesQ":[166],"enables":[167],"accurate":[168],"scenarios":[173],"while":[174],"still":[175],"maintains":[176],"competitive":[177],"performance":[178],"evaluation.":[181],"Extensive":[182],"experiments":[183],"demonstrate":[186],"superiority":[188],"proposed":[191],"method.":[192]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-03-06T00:00:00"}
