{"id":"https://openalex.org/W7140122354","doi":"https://doi.org/10.48550/arxiv.2603.19531","title":"dinov3.seg: Open-Vocabulary Semantic Segmentation with DINOv3","display_name":"dinov3.seg: Open-Vocabulary Semantic Segmentation with DINOv3","publication_year":2026,"publication_date":"2026-03-19","ids":{"openalex":"https://openalex.org/W7140122354","doi":"https://doi.org/10.48550/arxiv.2603.19531"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.19531","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.19531","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":"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.2603.19531","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5130369415","display_name":"Saikat Dutta","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dutta, Saikat","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130339737","display_name":"Biplab Banerjee","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Banerjee, Biplab","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5130327172","display_name":"Hamid Rezatofighi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Rezatofighi, Hamid","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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9670000076293945,"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":0.9670000076293945,"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":0.009100000374019146,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.006500000134110451,"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/leverage","display_name":"Leverage (statistics)","score":0.6542999744415283},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5931000113487244},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5799999833106995},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.5594000220298767},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.38690000772476196},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3799000084400177},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.35850000381469727},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.3580999970436096}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7922000288963318},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.680400013923645},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.6542999744415283},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5931000113487244},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5799999833106995},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.5594000220298767},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4077000021934509},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.38690000772476196},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3799000084400177},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.35850000381469727},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.3580999970436096},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.31630000472068787},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.3131999969482422},{"id":"https://openalex.org/C159620131","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Spatial analysis","level":2,"score":0.30720001459121704},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.303600013256073},{"id":"https://openalex.org/C130318100","wikidata":"https://www.wikidata.org/wiki/Q2268914","display_name":"Semantic similarity","level":2,"score":0.298799991607666},{"id":"https://openalex.org/C2777508537","wikidata":"https://www.wikidata.org/wiki/Q7936620","display_name":"Visual reasoning","level":2,"score":0.2973000109195709},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.29440000653266907},{"id":"https://openalex.org/C27511587","wikidata":"https://www.wikidata.org/wiki/Q2178623","display_name":"Spatial relation","level":2,"score":0.28760001063346863},{"id":"https://openalex.org/C48145219","wikidata":"https://www.wikidata.org/wiki/Q1335365","display_name":"Security token","level":2,"score":0.2786000072956085},{"id":"https://openalex.org/C64754055","wikidata":"https://www.wikidata.org/wiki/Q7574053","display_name":"Spatial contextual awareness","level":2,"score":0.27570000290870667},{"id":"https://openalex.org/C2775955345","wikidata":"https://www.wikidata.org/wiki/Q7449071","display_name":"Semantic mapping","level":2,"score":0.262800008058548},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.2596000134944916}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.19531","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.19531","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":"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.2603.19531","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.19531","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":"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":[{"score":0.46517887711524963,"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions"},{"score":0.4249398410320282,"id":"https://metadata.un.org/sdg/10","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":{"Open-Vocabulary":[0],"Semantic":[1],"Segmentation":[2],"(OVSS)":[3],"assigns":[4],"pixel-level":[5],"labels":[6],"from":[7,100,123],"an":[8],"open":[9],"set":[10],"of":[11,54,150,161],"text-defined":[12],"categories,":[13],"demanding":[14],"reliable":[15],"generalization":[16],"to":[17,47,92,154,207],"unseen":[18],"classes":[19],"at":[20],"inference.":[21],"Although":[22],"modern":[23],"vision-language":[24],"models":[25],"(VLMs)":[26],"support":[27],"strong":[28],"open-vocabulary":[29,102],"recognition,":[30],"their":[31],"representations":[32,152],"learned":[33],"through":[34],"global":[35,115,195],"contrastive":[36],"objectives":[37],"remain":[38],"suboptimal":[39],"for":[40,79],"dense":[41,172],"prediction,":[42],"prompting":[43],"many":[44],"OVSS":[45,205],"methods":[46],"depend":[48],"on":[49,141,186,201],"limited":[50],"adaptation":[51],"or":[52],"refinement":[53,149,160],"image-text":[55,155,164],"similarity":[56,144],"maps.":[57],"This,":[58],"in":[59,66,174],"turn,":[60],"restricts":[61],"spatial":[62,132,191],"precision":[63],"and":[64,118,170,216],"robustness":[65],"complex,":[67],"cluttered":[68,175],"scenes.":[69,176],"We":[70,197],"introduce":[71],"dinov3.seg,":[72],"extending":[73],"dinov3.txt":[74],"into":[75],"a":[76,88,180],"dedicated":[77],"framework":[78],"OVSS.":[80],"Our":[81],"contributions":[82],"are":[83],"four-fold.":[84],"First,":[85],"we":[86,106,146,178],"design":[87,98],"task-specific":[89],"architecture":[90],"tailored":[91],"this":[93],"backbone,":[94],"systematically":[95],"adapting":[96],"established":[97],"principles":[99],"prior":[101,136,153],"segmentation":[103],"work.":[104],"Second,":[105],"jointly":[107],"leverage":[108],"text":[109],"embeddings":[110],"aligned":[111],"with":[112,130],"both":[113],"the":[114,162],"[CLS]":[116],"token":[117],"local":[119],"patch-level":[120],"visual":[121,151],"features":[122],"ViT-based":[124],"encoder,":[125],"effectively":[126],"combining":[127],"semantic":[128],"discrimination":[129],"fine-grained":[131],"locality.":[133],"Third,":[134],"unlike":[135],"approaches":[137],"that":[138],"rely":[139],"primarily":[140],"post":[142],"hoc":[143],"refinement,":[145],"perform":[147],"early":[148],"interaction,":[156],"followed":[157],"by":[158],"late":[159],"resulting":[163],"correlation":[165],"features,":[166],"enabling":[167],"more":[168],"accurate":[169],"robust":[171],"predictions":[173],"Finally,":[177],"propose":[179],"high-resolution":[181],"local-global":[182],"inference":[183],"strategy":[184],"based":[185],"sliding-window":[187],"aggregation,":[188],"which":[189],"preserves":[190],"detail":[192],"while":[193],"maintaining":[194],"context.":[196],"conduct":[198],"extensive":[199],"experiments":[200],"five":[202],"widely":[203],"adopted":[204],"benchmarks":[206],"evaluate":[208],"our":[209],"approach.":[210],"The":[211],"results":[212],"demonstrate":[213],"its":[214],"effectiveness":[215],"robustness,":[217],"consistently":[218],"outperforming":[219],"current":[220],"state-of-the-art":[221],"methods.":[222]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-03-24T00:00:00"}