{"id":"https://openalex.org/W7160252466","doi":"https://doi.org/10.1109/wacv61042.2026.00093","title":"MEDAL: multi-modal MEta-space Distillation and ALignment for Visual Compatibility Learning","display_name":"MEDAL: multi-modal MEta-space Distillation and ALignment for Visual Compatibility Learning","publication_year":2026,"publication_date":"2026-03-06","ids":{"openalex":"https://openalex.org/W7160252466","doi":"https://doi.org/10.1109/wacv61042.2026.00093"},"language":null,"primary_location":{"id":"doi:10.1109/wacv61042.2026.00093","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wacv61042.2026.00093","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5078967141","display_name":"Dween Rabius Sanny","orcid":"https://orcid.org/0000-0001-6664-9361"},"institutions":[{"id":"https://openalex.org/I1311688040","display_name":"Amazon (United States)","ror":"https://ror.org/04mv4n011","country_code":"US","type":"company","lineage":["https://openalex.org/I1311688040"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Dween Rabius Sanny","raw_affiliation_strings":["Amazon,International Machine Learning,India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Amazon,International Machine Learning,India","institution_ids":["https://openalex.org/I1311688040"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101744115","display_name":"Vinay Kumar Verma","orcid":"https://orcid.org/0000-0002-6532-2997"},"institutions":[{"id":"https://openalex.org/I2800133710","display_name":"Discovery Centre","ror":"https://ror.org/02g5x8q86","country_code":"CA","type":"archive","lineage":["https://openalex.org/I2800133710"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Vinay Kumar Verma","raw_affiliation_strings":["Private Brands & Discovery"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Private Brands & Discovery","institution_ids":["https://openalex.org/I2800133710"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017163111","display_name":"Prateek Sircar","orcid":null},"institutions":[{"id":"https://openalex.org/I1311688040","display_name":"Amazon (United States)","ror":"https://ror.org/04mv4n011","country_code":"US","type":"company","lineage":["https://openalex.org/I1311688040"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Prateek Sircar","raw_affiliation_strings":["Amazon,International Machine Learning,India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Amazon,International Machine Learning,India","institution_ids":["https://openalex.org/I1311688040"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5034853815","display_name":"Deepak Gupta","orcid":"https://orcid.org/0000-0002-3019-7161"},"institutions":[{"id":"https://openalex.org/I1311688040","display_name":"Amazon (United States)","ror":"https://ror.org/04mv4n011","country_code":"US","type":"company","lineage":["https://openalex.org/I1311688040"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Deepak Gupta","raw_affiliation_strings":["Amazon,International Machine Learning,India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Amazon,International Machine Learning,India","institution_ids":["https://openalex.org/I1311688040"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"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":0.5090834,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"888","last_page":"897"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11714","display_name":"Multimodal Machine Learning Applications","score":0.8673999905586243,"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":0.8673999905586243,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.03180000185966492,"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"}},{"id":"https://openalex.org/T12031","display_name":"Speech and dialogue systems","score":0.014299999922513962,"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/compatibility","display_name":"Compatibility (geochemistry)","score":0.45329999923706055},{"id":"https://openalex.org/keywords/distillation","display_name":"Distillation","score":0.3472000062465668},{"id":"https://openalex.org/keywords/visualization","display_name":"Visualization","score":0.321399986743927},{"id":"https://openalex.org/keywords/visual-control","display_name":"Visual control","score":0.26809999346733093}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5752000212669373},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5286999940872192},{"id":"https://openalex.org/C2778648169","wikidata":"https://www.wikidata.org/wiki/Q967768","display_name":"Compatibility (geochemistry)","level":2,"score":0.45329999923706055},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.36959999799728394},{"id":"https://openalex.org/C204030448","wikidata":"https://www.wikidata.org/wiki/Q101017","display_name":"Distillation","level":2,"score":0.3472000062465668},{"id":"https://openalex.org/C199639397","wikidata":"https://www.wikidata.org/wiki/Q1788588","display_name":"Engineering drawing","level":1,"score":0.33000001311302185},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.321399986743927},{"id":"https://openalex.org/C133731056","wikidata":"https://www.wikidata.org/wiki/Q4917288","display_name":"Control engineering","level":1,"score":0.2921000123023987},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.27219998836517334},{"id":"https://openalex.org/C2780527621","wikidata":"https://www.wikidata.org/wiki/Q7936593","display_name":"Visual control","level":2,"score":0.26809999346733093}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/wacv61042.2026.00093","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wacv61042.2026.00093","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":33,"referenced_works":["https://openalex.org/W203260675","https://openalex.org/W2027731328","https://openalex.org/W2194775991","https://openalex.org/W2221507685","https://openalex.org/W2483053118","https://openalex.org/W2737102415","https://openalex.org/W2774458425","https://openalex.org/W2795117763","https://openalex.org/W2895347732","https://openalex.org/W2912053445","https://openalex.org/W2946617802","https://openalex.org/W2963258075","https://openalex.org/W2963900085","https://openalex.org/W2966334333","https://openalex.org/W2970053263","https://openalex.org/W2984373482","https://openalex.org/W3010209648","https://openalex.org/W3034366527","https://openalex.org/W3034891407","https://openalex.org/W3094502228","https://openalex.org/W3159481202","https://openalex.org/W3178643016","https://openalex.org/W4313529404","https://openalex.org/W4385800890","https://openalex.org/W4387954097","https://openalex.org/W4392271057","https://openalex.org/W4394592967","https://openalex.org/W4394597640","https://openalex.org/W4394625835","https://openalex.org/W4403577395","https://openalex.org/W4404612908","https://openalex.org/W4407953194","https://openalex.org/W4414198486"],"related_works":[],"abstract_inverted_index":{"Visual":[0],"compatibility":[1,55],"recommendation":[2],"systems":[3],"aim":[4],"to":[5,95,152,165,191,203],"surface":[6],"compatible":[7],"items":[8],"(e.g.":[9],"pants,":[10],"shoes)":[11],"that":[12,33,54,78,208],"harmonise":[13],"with":[14,128,137],"a":[15,47,75,87,92,182],"user-selected":[16],"product":[17],"(e.g.,":[18],"shirt).":[19],"Existing":[20],"methods":[21],"struggle":[22],"in":[23],"three":[24,81],"key":[25],"aspects:":[26],"they":[27,42,62],"rely":[28],"on":[29,147,178],"global":[30,105,167],"CNN":[31],"representations":[32],"overlook":[34],"fine-grained":[35],"local":[36],"cues":[37],"critical":[38],"for":[39,120],"visual":[40],"pairing;":[41],"force":[43],"all":[44,80],"categories":[45],"into":[46,114],"single":[48],"latent":[49],"space,":[50],"ignoring":[51],"the":[52,110,196],"fact":[53],"rules":[56],"differ":[57],"across":[58],"product-type":[59],"pairs;":[60],"and":[61,72,99,123,132,181,193,214],"demand":[63],"costly,":[64],"expert-annotated":[65],"outfit":[66],"labels.":[67],"We":[68,157],"introduce":[69],"MEDAL(Meta-space":[70],"Distillation":[71],"Alignment":[73],"),":[74],"self-supervised":[76],"framework":[77],"addresses":[79],"challenges":[82],"simultaneously.":[83],"MEDAL":[84,209],"(i)":[85],"employs":[86],"local\u2013global":[88],"augmentation":[89],"curriculum":[90],"inside":[91],"teacher\u2013student":[93],"ViT":[94],"emphasise":[96],"patch-level":[97],"texture":[98],"pattern":[100],"similarities":[101],"while":[102],"suppressing":[103],"confounding":[104],"shape":[106],"cues;":[107],"(ii)":[108],"partitions":[109],"joint":[111],"feature":[112],"manifold":[113],"learnable,":[115],"pair-specific":[116],"meta-spaces":[117],"so":[118],"that,":[119],"example,":[121],"{shirt,pants}":[122],"{pants,shoes}":[124],"relationships":[125],"are":[126],"modelled":[127],"distinct":[129],"projection":[130],"masks;":[131],"(iii)":[133],"replaces":[134],"manual":[135],"labels":[136],"distantly":[138],"supervised":[139],"KD,":[140],"harvesting":[141],"pseudo-compatible":[142],"sets":[143],"via":[144],"object":[145],"detection":[146],"web":[148],"images,":[149],"thus":[150],"scaling":[151],"millions":[153],"of":[154,189],"real-world":[155],"examples.":[156],"further":[158],"fuse":[159],"perceptually":[160],"uniform":[161],"LUV":[162],"colour":[163,168,218],"histograms":[164],"capture":[166],"harmony":[169],"often":[170],"missed":[171],"by":[172],"pure":[173],"vision":[174],"transformers.":[175],"Extensive":[176],"experiments":[177],"Polyvore":[179],"disjoint/non-disjoint":[180],"2M-image":[183],"in-house":[184],"dataset":[185],"show":[186],"state-of-the-art":[187],"gains":[188],"up":[190],"+3.72/+2.7FITB":[192],"+9.58R@10":[194],"over":[195],"strongest":[197],"baseline,":[198],"whilst":[199],"cutting":[200],"annotation":[201],"cost":[202],"zero.":[204],"Qualitative":[205],"studies":[206],"confirm":[207],"retrieves":[210],"stylistically":[211],"coherent":[212],"outfits":[213],"correctly":[215],"penalises":[216],"mismatched":[217],"palettes.":[219]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2026-05-06T00:00:00"}