{"id":"https://openalex.org/W7162335996","doi":"https://doi.org/10.48550/arxiv.2605.23656","title":"Recursive Block-Diagonal Coupling for Resource-Efficient Training of Vision Models","display_name":"Recursive Block-Diagonal Coupling for Resource-Efficient Training of Vision Models","publication_year":2026,"publication_date":"2026-05-22","ids":{"openalex":"https://openalex.org/W7162335996","doi":"https://doi.org/10.48550/arxiv.2605.23656"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.23656","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.23656","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":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.2605.23656","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5039278269","display_name":"Maxim Henry","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Henry, Maxim","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5067516436","display_name":"Adrien Deli\u00e8ge","orcid":"https://orcid.org/0000-0003-3981-6982"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Deli\u00e8ge, Adrien","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128673582","display_name":"S\u00e9bastien Pierard","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pi\u00e9rard, S\u00e9bastien","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5047102584","display_name":"Marc Van Droogenbroeck","orcid":"https://orcid.org/0000-0001-6260-6487"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Van Droogenbroeck, Marc","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/T10036","display_name":"Advanced Neural Network Applications","score":0.9079999923706055,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9079999923706055,"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":0.023499999195337296,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.006800000090152025,"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/flops","display_name":"FLOPS","score":0.8382999897003174},{"id":"https://openalex.org/keywords/scratch","display_name":"Scratch","score":0.6121000051498413},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.5845000147819519},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.4648999869823456},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.38989999890327454},{"id":"https://openalex.org/keywords/bridging","display_name":"Bridging (networking)","score":0.38280001282691956},{"id":"https://openalex.org/keywords/reduction","display_name":"Reduction (mathematics)","score":0.34769999980926514},{"id":"https://openalex.org/keywords/computational-complexity-theory","display_name":"Computational complexity theory","score":0.3343000113964081}],"concepts":[{"id":"https://openalex.org/C3826847","wikidata":"https://www.wikidata.org/wiki/Q188768","display_name":"FLOPS","level":2,"score":0.8382999897003174},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7468000054359436},{"id":"https://openalex.org/C2781235140","wikidata":"https://www.wikidata.org/wiki/Q275131","display_name":"Scratch","level":2,"score":0.6121000051498413},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.5845000147819519},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5175999999046326},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.4648999869823456},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.38989999890327454},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.38280001282691956},{"id":"https://openalex.org/C174348530","wikidata":"https://www.wikidata.org/wiki/Q188635","display_name":"Bridging (networking)","level":2,"score":0.38280001282691956},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.34769999980926514},{"id":"https://openalex.org/C179799912","wikidata":"https://www.wikidata.org/wiki/Q205084","display_name":"Computational complexity theory","level":2,"score":0.3343000113964081},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.3244999945163727},{"id":"https://openalex.org/C131584629","wikidata":"https://www.wikidata.org/wiki/Q4308705","display_name":"Coupling (piping)","level":2,"score":0.3181999921798706},{"id":"https://openalex.org/C66024118","wikidata":"https://www.wikidata.org/wiki/Q1122506","display_name":"Computational model","level":2,"score":0.3109000027179718},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.30410000681877136},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.30239999294281006},{"id":"https://openalex.org/C206729178","wikidata":"https://www.wikidata.org/wiki/Q2271896","display_name":"Scheduling (production processes)","level":2,"score":0.2994999885559082},{"id":"https://openalex.org/C2780385302","wikidata":"https://www.wikidata.org/wiki/Q367158","display_name":"Protocol (science)","level":3,"score":0.289000004529953},{"id":"https://openalex.org/C5339829","wikidata":"https://www.wikidata.org/wiki/Q1425977","display_name":"Machine vision","level":2,"score":0.28859999775886536},{"id":"https://openalex.org/C64876066","wikidata":"https://www.wikidata.org/wiki/Q5141226","display_name":"Cognitive neuroscience of visual object recognition","level":3,"score":0.27869999408721924},{"id":"https://openalex.org/C2779714256","wikidata":"https://www.wikidata.org/wiki/Q25305062","display_name":"Multiple Models","level":2,"score":0.26750001311302185},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.26260000467300415},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.25870001316070557}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.23656","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.23656","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":"doi:10.48550/arxiv.2605.23656","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.23656","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/8","score":0.4140103757381439,"display_name":"Decent work and economic growth"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Training":[0],"high-capacity":[1],"vision":[2,81],"models":[3,48,59,77,100,138],"from":[4,102,128],"scratch":[5,103],"requires":[6],"substantial":[7],"computational":[8,32],"resources.":[9],"To":[10],"improve":[11],"training":[12,42,71,92,123,126],"efficiency":[13,98],"of":[14,26,34,69],"a":[15,52,61,66,95],"wide":[16,47],"target":[17],"model,":[18],"existing":[19],"growth":[20,131],"methods":[21],"often":[22],"assume":[23],"the":[24,30,35,70,76,105,129],"availability":[25],"narrower":[27],"models,":[28],"obscuring":[29],"true":[31],"cost":[33],"entire":[36],"pipeline.":[37],"We":[38],"propose":[39],"an":[40],"efficient":[41],"protocol,":[43,107],"RBDC,":[44],"that":[45,136],"builds":[46],"by":[49],"coupling":[50],"in":[51,60],"parameter-free":[53],"block-diagonal":[54],"way":[55],"narrower,":[56],"independently":[57],"trained":[58,101],"recursive":[62],"way.":[63],"This":[64],"allows":[65],"flexible":[67],"allocation":[68],"budget":[72],"available":[73],"across":[74],"all":[75],"involved.":[78],"Evaluated":[79],"with":[80,104],"transformers":[82],"(DeiT)":[83],"and":[84,152],"convolutional":[85],"networks":[86],"(ResNet)":[87],"on":[88],"ImageNet,":[89],"our":[90,137],"RBDC":[91],"protocol":[93],"shows":[94],"much":[96],"better":[97,142],"than":[99,125,144],"standard":[106],"yielding":[108],"30%":[109],"FLOPs":[110,124],"reduction":[111],"at":[112,121],"similar":[113],"test":[114],"accuracies.":[115],"It":[116],"also":[117],"achieves":[118],"higher":[119],"performances":[120],"same":[122],"protocols":[127],"model":[130],"literature.":[132],"Finally,":[133],"we":[134],"show":[135],"can":[139],"serve":[140],"as":[141],"backbones":[143],"their":[145],"original":[146],"counterparts":[147],"for":[148],"downstream":[149],"object":[150],"detection":[151],"instance":[153],"segmentation":[154],"tasks.":[155]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-26T00:00:00"}
