{"id":"https://openalex.org/W7162538619","doi":"https://doi.org/10.48550/arxiv.2605.26656","title":"DV-SFT: Direct Vision Supervision for Fine-Grained Visual Understanding","display_name":"DV-SFT: Direct Vision Supervision for Fine-Grained Visual Understanding","publication_year":2026,"publication_date":"2026-05-26","ids":{"openalex":"https://openalex.org/W7162538619","doi":"https://doi.org/10.48550/arxiv.2605.26656"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.26656","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.26656","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.2605.26656","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5137143746","display_name":"Jianfei Zhao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhao, Jianfei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137117781","display_name":"Feng Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Feng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137159249","display_name":"Xin Sun","orcid":"https://orcid.org/0009-0006-3220-8166"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sun, Xin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137143644","display_name":"Chong Feng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Feng, Chong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137109187","display_name":"Bing Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Bing","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5086583247","display_name":"Zhixing Tan","orcid":"https://orcid.org/0000-0002-2426-6220"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tan, Zhixing","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":0.9222999811172485,"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":0.9222999811172485,"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/T10028","display_name":"Topic Modeling","score":0.02630000002682209,"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/T10601","display_name":"Handwritten Text Recognition Techniques","score":0.007300000172108412,"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/security-token","display_name":"Security token","score":0.5419999957084656},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.5338000059127808},{"id":"https://openalex.org/keywords/visual-perception","display_name":"Visual perception","score":0.35519999265670776},{"id":"https://openalex.org/keywords/human-visual-system-model","display_name":"Human visual system model","score":0.35440000891685486},{"id":"https://openalex.org/keywords/core","display_name":"Core (optical fiber)","score":0.35199999809265137},{"id":"https://openalex.org/keywords/word","display_name":"Word (group theory)","score":0.3384999930858612},{"id":"https://openalex.org/keywords/visualization","display_name":"Visualization","score":0.2915000021457672}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7448999881744385},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5888000130653381},{"id":"https://openalex.org/C48145219","wikidata":"https://www.wikidata.org/wiki/Q1335365","display_name":"Security token","level":2,"score":0.5419999957084656},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.5338000059127808},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.43689998984336853},{"id":"https://openalex.org/C178253425","wikidata":"https://www.wikidata.org/wiki/Q162668","display_name":"Visual perception","level":3,"score":0.35519999265670776},{"id":"https://openalex.org/C160086991","wikidata":"https://www.wikidata.org/wiki/Q5939193","display_name":"Human visual system model","level":3,"score":0.35440000891685486},{"id":"https://openalex.org/C2164484","wikidata":"https://www.wikidata.org/wiki/Q5170150","display_name":"Core (optical fiber)","level":2,"score":0.35199999809265137},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.33880001306533813},{"id":"https://openalex.org/C90805587","wikidata":"https://www.wikidata.org/wiki/Q10944557","display_name":"Word (group theory)","level":2,"score":0.3384999930858612},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.325300008058548},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.2915000021457672},{"id":"https://openalex.org/C164280684","wikidata":"https://www.wikidata.org/wiki/Q5529040","display_name":"Gaze-contingency paradigm","level":4,"score":0.28519999980926514},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.2831999957561493},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.2741999924182892},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.2700999975204468},{"id":"https://openalex.org/C2780878386","wikidata":"https://www.wikidata.org/wiki/Q1659648","display_name":"Visual language","level":2,"score":0.26980000734329224},{"id":"https://openalex.org/C168820333","wikidata":"https://www.wikidata.org/wiki/Q448889","display_name":"Visual inspection","level":2,"score":0.26809999346733093},{"id":"https://openalex.org/C2778251979","wikidata":"https://www.wikidata.org/wiki/Q7936617","display_name":"Visual processing","level":3,"score":0.2533999979496002}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.26656","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.26656","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.2605.26656","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.26656","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":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Multimodal":[0],"large":[1],"language":[2],"models":[3],"are":[4,15,29],"typically":[5],"trained":[6],"end-to-end":[7],"to":[8,18,39,46],"predict":[9],"ground-truth":[10],"answers,":[11],"yet":[12],"supervision":[13,89,171],"signals":[14],"applied":[16],"exclusively":[17],"text":[19],"tokens.":[20],"Visual":[21],"tokens,":[22],"the":[23,36,97,108,122,131,148],"core":[24],"carriers":[25],"of":[26,35,150],"visual":[27,41,48,64,91,119,175],"information,":[28],"optimized":[30],"only":[31],"implicitly":[32],"as":[33,57,133],"part":[34],"context,":[37],"leading":[38],"coarse-grained":[40],"understanding.":[42],"Prior":[43],"works":[44],"attempt":[45],"supervise":[47],"inputs":[49],"but":[50],"inevitably":[51],"rely":[52],"on":[53],"auxiliary":[54],"components":[55],"such":[56],"additional":[58,142],"decoders":[59],"or":[60,141],"forward":[61,143],"passes,":[62],"because":[63],"tokens":[65,92],"lack":[66],"readily":[67],"interpretable":[68],"labels.":[69],"This":[70],"limits":[71],"their":[72],"practical":[73],"applicability.":[74],"In":[75],"this":[76],"work,":[77],"we":[78,106],"propose":[79],"\\textbf{D}irect":[80],"\\textbf{V}ision":[81],"\\textbf{S}upervised":[82],"\\textbf{F}ine-\\textbf{T}uning":[83],"(DV-SFT),":[84],"which":[85],"constructs":[86],"explicit,":[87],"token-level":[88],"for":[90,103],"and":[93,115,162,177],"trains":[94],"them":[95],"through":[96],"same":[98],"next-token":[99],"prediction":[100],"objective":[101],"used":[102],"text.":[104],"Specifically,":[105],"exploit":[107],"direct":[109,151],"vision--text":[110],"correspondence":[111],"in":[112,124],"OCR-related":[113],"scenarios":[114],"automatically":[116],"label":[117],"each":[118],"token":[120],"with":[121],"word":[123],"its":[125],"corresponding":[126],"image":[127],"patch.":[128],"DV-SFT":[129,154],"treats":[130],"MLLM":[132],"a":[134],"black":[135],"box,":[136],"requiring":[137],"no":[138],"architectural":[139],"modifications":[140],"passes.":[144],"Extensive":[145],"experiments":[146],"demonstrate":[147],"superiority":[149],"vision":[152,170],"supervision.":[153],"consistently":[155],"outperforms":[156],"standard":[157],"SFT":[158],"across":[159],"three":[160],"in-domain":[161],"four":[163],"out-of-domain":[164],"benchmarks.":[165],"Further":[166],"analyses":[167],"show":[168],"that":[169],"effectively":[172],"enhances":[173],"fine-grained":[174],"understanding":[176],"achieves":[178],"higher":[179],"multimodal":[180],"alignment":[181],"efficiency.":[182]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-28T00:00:00"}