{"id":"https://openalex.org/W7160871312","doi":"https://doi.org/10.48550/arxiv.2605.07786","title":"APEX: Assumption-free Projection-based Embedding eXamination Metric for Image Quality Assessment","display_name":"APEX: Assumption-free Projection-based Embedding eXamination Metric for Image Quality Assessment","publication_year":2026,"publication_date":"2026-05-08","ids":{"openalex":"https://openalex.org/W7160871312","doi":"https://doi.org/10.48550/arxiv.2605.07786"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.07786","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.07786","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.07786","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5098911059","display_name":"Caterina Gallegati","orcid":"https://orcid.org/0009-0008-9975-5038"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gallegati, Caterina","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5091787895","display_name":"Monica Bianchini","orcid":"https://orcid.org/0000-0002-8206-8142"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bianchini, Monica","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5020560480","display_name":"Franco Scarselli","orcid":"https://orcid.org/0000-0003-1307-0772"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Scarselli, Franco","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135830962","display_name":"Vittorio Murino","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Murino, Vittorio","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5044252882","display_name":"Barbara Toniella Corradini","orcid":"https://orcid.org/0000-0003-3568-5874"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Corradini, Barbara Toniella","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":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"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":0.32330000400543213,"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":0.32330000400543213,"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/T11165","display_name":"Image and Video Quality Assessment","score":0.2978000044822693,"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":0.18559999763965607,"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/robustness","display_name":"Robustness (evolution)","score":0.5867000222206116},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.5428000092506409},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.4747999906539917},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.4343999922275543},{"id":"https://openalex.org/keywords/parametric-statistics","display_name":"Parametric statistics","score":0.4253000020980835},{"id":"https://openalex.org/keywords/benchmarking","display_name":"Benchmarking","score":0.4246000051498413},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4092999994754791},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.3515999913215637},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.35040000081062317}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6937000155448914},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5916000008583069},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.5867000222206116},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.5428000092506409},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.4747999906539917},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.4343999922275543},{"id":"https://openalex.org/C117251300","wikidata":"https://www.wikidata.org/wiki/Q1849855","display_name":"Parametric statistics","level":2,"score":0.4253000020980835},{"id":"https://openalex.org/C86251818","wikidata":"https://www.wikidata.org/wiki/Q816754","display_name":"Benchmarking","level":2,"score":0.4246000051498413},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4092999994754791},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.38119998574256897},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.36149999499320984},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.3515999913215637},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.35040000081062317},{"id":"https://openalex.org/C2780513914","wikidata":"https://www.wikidata.org/wiki/Q18210350","display_name":"Bottleneck","level":2,"score":0.34299999475479126},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3418999910354614},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.3328000009059906},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.3244999945163727},{"id":"https://openalex.org/C101814296","wikidata":"https://www.wikidata.org/wiki/Q5439685","display_name":"Feature model","level":3,"score":0.3142000138759613},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.2973000109195709},{"id":"https://openalex.org/C2983787585","wikidata":"https://www.wikidata.org/wiki/Q93586","display_name":"Feature matching","level":3,"score":0.28940001130104065},{"id":"https://openalex.org/C55020928","wikidata":"https://www.wikidata.org/wiki/Q3813865","display_name":"Image quality","level":3,"score":0.2833000123500824},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.28189998865127563},{"id":"https://openalex.org/C2780719617","wikidata":"https://www.wikidata.org/wiki/Q1030752","display_name":"Salient","level":2,"score":0.27379998564720154},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.26829999685287476},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.26030001044273376},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.25440001487731934},{"id":"https://openalex.org/C3020001037","wikidata":"https://www.wikidata.org/wiki/Q836575","display_name":"Quality assessment","level":3,"score":0.25290000438690186}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.07786","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.07786","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.07786","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.07786","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"As":[0],"generative":[1],"models":[2],"achieve":[3],"unprecedented":[4],"visual":[5,124],"quality,":[6],"the":[7,26,33,47,73],"gold":[8],"standard":[9],"for":[10],"image":[11],"evaluation":[12,70],"remains":[13],"traditional":[14],"feature-distribution":[15],"metrics":[16,21,131],"(e.g.,":[17],"FID).":[18],"However,":[19],"these":[20],"are":[22],"provably":[23],"hindered":[24],"by":[25],"closed-vocabulary":[27],"bottleneck":[28],"of":[29,36],"outdated":[30],"features":[31],"and":[32,96,103,110,134],"assumptive":[34],"bias":[35],"rigid":[37],"parametric":[38,55],"formulations.":[39],"Recent":[40],"alternatives":[41],"exploit":[42],"modern":[43],"backbones":[44],"to":[45,52,88,123],"solve":[46],"feature":[48,113],"bottleneck,":[49],"yet":[50],"continue":[51],"suffer":[53],"from":[54],"limitations.":[56],"To":[57],"close":[58],"this":[59],"gap,":[60],"we":[61,92,127],"introduce":[62],"APEX":[63,84,100,116,130],"(Assumption-free":[64],"Projection-based":[65],"Embedding":[66],"eXamination),":[67],"a":[68,78],"novel":[69],"framework":[71],"leveraging":[72],"Sliced":[74],"Wasserstein":[75],"Distance":[76],"as":[77,91,112],"mathematically":[79],"grounded,":[80],"assumption-free":[81],"similarity":[82],"measure.":[83],"inherits":[85],"effective":[86],"scalability":[87],"high-dimensional":[89],"spaces,":[90],"prove":[93],"with":[94],"theoretical":[95],"empirical":[97],"evidences.":[98],"Moreover,":[99],"is":[101],"embedding-agnostic":[102],"uses":[104],"two":[105],"open-vocabulary":[106],"foundation":[107],"models,":[108],"CLIP":[109],"DINOv2,":[111],"extractors.":[114],"Benchmarking":[115],"against":[117],"established":[118],"baselines":[119],"reveals":[120],"superior":[121],"robustness":[122],"degradations.":[125],"Additionally,":[126],"show":[128],"that":[129],"exhibit":[132],"intra-":[133],"cross-dataset":[135],"stability,":[136],"ensuring":[137],"highly":[138],"stable":[139],"evaluations":[140],"on":[141],"out-of-domain":[142],"datasets.":[143]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-05-12T00:00:00"}
