{"id":"https://openalex.org/W7164029907","doi":"https://doi.org/10.48550/arxiv.2606.07645","title":"FineGen: A VLM-based Multi-Agent Framework for Fine-Grained Image-Text Dataset Construction","display_name":"FineGen: A VLM-based Multi-Agent Framework for Fine-Grained Image-Text Dataset Construction","publication_year":2026,"publication_date":"2026-06-02","ids":{"openalex":"https://openalex.org/W7164029907","doi":"https://doi.org/10.48550/arxiv.2606.07645"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.07645","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.07645","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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.2606.07645","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5138250077","display_name":"Chang Kong","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kong, Chang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5125545984","display_name":"Yuebing Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Yuebing","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138239112","display_name":"Peng Mo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mo, Peng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138248770","display_name":"Haigang Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Haigang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5100901719","display_name":"Qiuming Luo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Luo, Qiuming","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":0.9520000219345093,"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":0.9520000219345093,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.01269999984651804,"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":0.008299999870359898,"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/pipeline","display_name":"Pipeline (software)","score":0.7161999940872192},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.6523000001907349},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6383000016212463},{"id":"https://openalex.org/keywords/measure","display_name":"Measure (data warehouse)","score":0.33410000801086426},{"id":"https://openalex.org/keywords/visualization","display_name":"Visualization","score":0.30390000343322754}],"concepts":[{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.7161999940872192},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7105000019073486},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.6523000001907349},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6383000016212463},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.599399983882904},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4198000133037567},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4032000005245209},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.33410000801086426},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.30390000343322754},{"id":"https://openalex.org/C109747225","wikidata":"https://www.wikidata.org/wiki/Q815758","display_name":"Scarcity","level":2,"score":0.2955999970436096},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.2815999984741211},{"id":"https://openalex.org/C2776207758","wikidata":"https://www.wikidata.org/wiki/Q5303302","display_name":"Downstream (manufacturing)","level":2,"score":0.27399998903274536}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.07645","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.07645","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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.2606.07645","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.07645","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":{"The":[0],"scarcity":[1],"of":[2],"hard":[3,42,67,102],"negative":[4],"samples":[5],"in":[6],"current":[7],"vision-language":[8],"datasets":[9],"significantly":[10,104],"hinders":[11],"fine-grained":[12],"perception.":[13],"To":[14],"address":[15],"this,":[16],"we":[17,57],"propose":[18],"FineGen,":[19],"a":[20,30,35,60,70,78,96],"VLM-based":[21],"Multi-Agent":[22],"framework":[23],"for":[24],"automated":[25],"dataset":[26,62],"construction.":[27],"By":[28],"employing":[29],"collaborative":[31],"Generation-Verification-Correction":[32],"pipeline":[33],"with":[34,69],"closed-loop":[36],"feedback":[37],"mechanism,":[38],"FineGen":[39],"ensures":[40],"synthesized":[41],"negatives":[43,68],"are":[44],"semantically":[45],"valid":[46],"yet":[47],"strictly":[48],"contradictory":[49],"to":[50,55],"visual":[51],"content.":[52],"Applying":[53],"this":[54],"ImageNet,":[56],"construct":[58],"FineGen-100K,":[59],"hierarchical":[61],"containing":[63],"over":[64],"147,000":[65],"attribute-specific":[66],"rigorous":[71],"1:10":[72],"positive-to-negative":[73],"ratio.":[74],"Extensive":[75],"evaluations":[76],"confirm":[77],"96.7%":[79],"attribute":[80],"validity":[81],"rate.":[82],"Crucially,":[83],"downstream":[84],"validation":[85],"on":[86,93,101],"the":[87],"FG-OVD":[88],"benchmark":[89],"shows":[90],"that":[91],"fine-tuning":[92],"FineGen-100K":[94],"yields":[95],"substantial":[97],"+14.4%":[98],"accuracy":[99],"improvement":[100],"samples,":[103],"outperforming":[105],"state-of-the-art":[106],"methods.":[107]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-06-10T00:00:00"}