{"id":"https://openalex.org/W7154473693","doi":"https://doi.org/10.48550/arxiv.2604.12935","title":"Task Alignment: A simple and effective proxy for model merging in computer vision","display_name":"Task Alignment: A simple and effective proxy for model merging in computer vision","publication_year":2026,"publication_date":"2026-04-14","ids":{"openalex":"https://openalex.org/W7154473693","doi":"https://doi.org/10.48550/arxiv.2604.12935"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.12935","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.12935","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.2604.12935","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5133701621","display_name":"Pau de Jorge","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"de Jorge, Pau","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006314986","display_name":"Souza Cesar De","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"de Souza, C\u00e9sar Roberto","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5061281094","display_name":"Bj\u00f6rn Michele","orcid":"https://orcid.org/0009-0004-1902-6232"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Michele, Bj\u00f6rn","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5059863828","display_name":"Mert B\u00fclent Sar\u0131y\u0131ld\u0131z","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sar\u0131y\u0131ld\u0131z, Mert B\u00fclent","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054497181","display_name":"Philippe Weinzaepfel","orcid":"https://orcid.org/0000-0002-4223-3983"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Weinzaepfel, Philippe","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5059987704","display_name":"Florent Perronnin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Perronnin, Florent","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5036113570","display_name":"Diane Larlus","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Larlus, Diane","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5042126127","display_name":"Yannis Kalantidis","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kalantidis, Yannis","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":0.6297000050544739,"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":0.6297000050544739,"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":0.10689999908208847,"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":0.05130000039935112,"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/hyperparameter","display_name":"Hyperparameter","score":0.7181000113487244},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.5881999731063843},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.461899995803833},{"id":"https://openalex.org/keywords/model-selection","display_name":"Model selection","score":0.46050000190734863},{"id":"https://openalex.org/keywords/proxy","display_name":"Proxy (statistics)","score":0.44600000977516174},{"id":"https://openalex.org/keywords/relevance","display_name":"Relevance (law)","score":0.412200003862381},{"id":"https://openalex.org/keywords/statistical-model","display_name":"Statistical model","score":0.3921000063419342}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8148000240325928},{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.7181000113487244},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6460000276565552},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.611299991607666},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.5881999731063843},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.461899995803833},{"id":"https://openalex.org/C93959086","wikidata":"https://www.wikidata.org/wiki/Q6888345","display_name":"Model selection","level":2,"score":0.46050000190734863},{"id":"https://openalex.org/C2780148112","wikidata":"https://www.wikidata.org/wiki/Q1432581","display_name":"Proxy (statistics)","level":2,"score":0.44600000977516174},{"id":"https://openalex.org/C158154518","wikidata":"https://www.wikidata.org/wiki/Q7310970","display_name":"Relevance (law)","level":2,"score":0.412200003862381},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.3921000063419342},{"id":"https://openalex.org/C155846161","wikidata":"https://www.wikidata.org/wiki/Q1143367","display_name":"Graphical model","level":2,"score":0.3305000066757202},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.31349998712539673},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.31139999628067017},{"id":"https://openalex.org/C2780586882","wikidata":"https://www.wikidata.org/wiki/Q7520643","display_name":"Simple (philosophy)","level":2,"score":0.30480000376701355},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3009999990463257},{"id":"https://openalex.org/C5339829","wikidata":"https://www.wikidata.org/wiki/Q1425977","display_name":"Machine vision","level":2,"score":0.2930999994277954},{"id":"https://openalex.org/C2779714256","wikidata":"https://www.wikidata.org/wiki/Q25305062","display_name":"Multiple Models","level":2,"score":0.2806999981403351},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.27559998631477356},{"id":"https://openalex.org/C75553542","wikidata":"https://www.wikidata.org/wiki/Q178161","display_name":"A priori and a posteriori","level":2,"score":0.27390000224113464}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.12935","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.12935","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.2604.12935","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.12935","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":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Efficiently":[0],"merging":[1,29,56,152],"several":[2],"models":[3,92,156],"fine-tuned":[4],"for":[5],"different":[6,41,45,74],"tasks,":[7],"but":[8],"stemming":[9],"from":[10,84],"the":[11,98,117,142,148],"same":[12],"pretrained":[13],"base":[14],"model,":[15],"is":[16,52],"of":[17,27,101,135,150],"great":[18],"practical":[19,58],"interest.":[20],"Despite":[21],"extensive":[22],"prior":[23],"work,":[24,49],"most":[25,71],"evaluations":[26],"model":[28,55,151],"in":[30],"computer":[31],"vision":[32,72,155],"are":[33],"restricted":[34],"to":[35,53,128,153],"image":[36],"classification":[37,42],"using":[38],"CLIP,":[39],"where":[40,90],"datasets":[43],"define":[44],"tasks.":[46],"In":[47,70],"this":[48,67],"our":[50],"goal":[51],"make":[54],"more":[57],"and":[59,79,121],"show":[60,122],"its":[61],"relevance":[62],"on":[63,77,108],"challenging":[64],"scenarios":[65],"beyond":[66,157],"specific":[68],"setting.":[69],"scenarios,":[73],"tasks":[75],"rely":[76],"trainable":[78],"usually":[80],"heterogeneous":[81],"decoders.":[82],"Differently":[83],"previous":[85],"studies":[86],"with":[87,141],"frozen":[88],"decoders,":[89],"merged":[91],"can":[93,125],"be":[94,126],"evaluated":[95],"right":[96],"away,":[97],"non-trivial":[99],"cost":[100],"decoder":[102],"training":[103],"renders":[104],"hyperparameter":[105,131],"selection":[106,132],"based":[107],"downstream":[109],"performance":[110],"impractical.":[111],"To":[112],"address":[113],"this,":[114],"we":[115,146],"introduce":[116],"task":[118,143],"alignment":[119,144],"proxy,":[120,145],"how":[123],"it":[124],"used":[127],"speed":[129],"up":[130],"by":[133],"orders":[134],"magnitude":[136],"while":[137],"retaining":[138],"performance.":[139],"Equipped":[140],"extend":[147],"applicability":[149],"multi-task":[154],"CLIP-based":[158],"classification.":[159]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-04-16T00:00:00"}