{"id":"https://openalex.org/W4416344262","doi":"https://doi.org/10.48550/arxiv.2511.11440","title":"From Synthetic Scenes to Real Performance: Enhancing Spatial Reasoning in VLMs","display_name":"From Synthetic Scenes to Real Performance: Enhancing Spatial Reasoning in VLMs","publication_year":2025,"publication_date":"2025-11-14","ids":{"openalex":"https://openalex.org/W4416344262","doi":"https://doi.org/10.48550/arxiv.2511.11440"},"language":null,"primary_location":{"id":"pmh:oai:arXiv.org:2511.11440","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2511.11440","pdf_url":"https://arxiv.org/pdf/2511.11440","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2511.11440","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5050043052","display_name":"Massimo Rizzoli","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Rizzoli, Massimo","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5093660464","display_name":"Simone Alghisi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Alghisi, Simone","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5047699997","display_name":"Seyed Mahed Mousavi","orcid":"https://orcid.org/0000-0002-7790-7730"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mousavi, Seyed Mahed","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5062879885","display_name":"Giuseppe Riccardi","orcid":"https://orcid.org/0000-0002-0739-8184"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Riccardi, Giuseppe","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5050043052"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"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":0.9526000022888184,"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":0.9526000022888184,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.009999999776482582,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.004999999888241291,"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/overfitting","display_name":"Overfitting","score":0.7730000019073486},{"id":"https://openalex.org/keywords/synthetic-data","display_name":"Synthetic data","score":0.7095000147819519},{"id":"https://openalex.org/keywords/annotation","display_name":"Annotation","score":0.6866999864578247},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.54830002784729},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.4406000077724457},{"id":"https://openalex.org/keywords/position","display_name":"Position (finance)","score":0.42879998683929443},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.38119998574256897}],"concepts":[{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.7730000019073486},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7533000111579895},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.7095000147819519},{"id":"https://openalex.org/C2776321320","wikidata":"https://www.wikidata.org/wiki/Q857525","display_name":"Annotation","level":2,"score":0.6866999864578247},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.67330002784729},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.54830002784729},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.48350000381469727},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.4406000077724457},{"id":"https://openalex.org/C198082294","wikidata":"https://www.wikidata.org/wiki/Q3399648","display_name":"Position (finance)","level":2,"score":0.42879998683929443},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4189999997615814},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.38119998574256897},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.37529999017715454},{"id":"https://openalex.org/C61272859","wikidata":"https://www.wikidata.org/wiki/Q7834031","display_name":"Transferability","level":3,"score":0.37139999866485596},{"id":"https://openalex.org/C75917345","wikidata":"https://www.wikidata.org/wiki/Q2725298","display_name":"Sampling bias","level":3,"score":0.3659000098705292},{"id":"https://openalex.org/C2775924081","wikidata":"https://www.wikidata.org/wiki/Q55608371","display_name":"Control (management)","level":2,"score":0.3549000024795532},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3310000002384186},{"id":"https://openalex.org/C133462117","wikidata":"https://www.wikidata.org/wiki/Q4929239","display_name":"Data collection","level":2,"score":0.30059999227523804},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2565000057220459}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2511.11440","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2511.11440","pdf_url":"https://arxiv.org/pdf/2511.11440","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2511.11440","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2511.11440","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":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2511.11440","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2511.11440","pdf_url":"https://arxiv.org/pdf/2511.11440","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4416344262.pdf","grobid_xml":"https://content.openalex.org/works/W4416344262.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Fine-tuning":[0],"Vision-Language":[1],"Models":[2],"(VLMs)":[3],"is":[4,24,86],"a":[5,40],"common":[6,166],"strategy":[7],"to":[8,27,45,127],"improve":[9],"performance":[10,125,159,175],"following":[11],"an":[12],"ad-hoc":[13],"data":[14,80,129,156,180],"collection":[15],"and":[16,30,36,59,81,92,109,123,142,164,168],"annotation":[17,60,93],"of":[18,79],"real-world":[19,128,143,179],"scenes.":[20],"However,":[21],"this":[22,47,116],"process":[23,70],"often":[25],"prone":[26],"biases,":[28],"errors,":[29],"distribution":[31,57,90],"imbalance,":[32,91],"resulting":[33],"in":[34,71],"overfitting":[35],"imbalanced":[37],"performance.":[38],"Although":[39],"few":[41],"studies":[42],"have":[43],"tried":[44],"address":[46,63],"problem":[48],"by":[49,100,176],"generating":[50],"synthetic":[51,141,155,172],"data,":[52],"they":[53],"lacked":[54],"control":[55,76],"over":[56],"bias":[58],"quality.":[61],"To":[62],"these":[64],"challenges,":[65],"we":[66,75,119],"redesign":[67],"the":[68,77,98,112,131,161,186],"fine-tuning":[69,152,170],"two":[72,148],"ways.":[73],"First,":[74],"generation":[78],"its":[82],"annotations,":[83],"ensuring":[84],"it":[85],"free":[87],"from":[88],"bias,":[89],"errors.":[94],"We":[95,135],"automatically":[96],"construct":[97],"dataset":[99],"comprehensively":[101],"sampling":[102],"objects'":[103],"attributes,":[104],"including":[105],"color,":[106],"shape,":[107],"size,":[108],"position":[110,133],"within":[111],"scene.":[113],"Secondly,":[114],"using":[115],"annotated":[117],"dataset,":[118],"fine-tune":[120],"state-of-the-art":[121],"VLMs":[122],"assess":[124],"transferability":[126],"on":[130,139,153,171,178,185],"absolute":[132],"task.":[134],"conduct":[136],"exhaustive":[137],"evaluations":[138],"both":[140],"benchmarks.":[144],"Our":[145],"experiments":[146],"reveal":[147],"key":[149],"findings:":[150],"1)":[151],"balanced":[154],"yields":[157],"uniform":[158],"across":[160],"visual":[162],"scene":[163],"mitigates":[165],"biases;":[167],"2)":[169],"stimuli":[173],"improves":[174],"13%":[177],"(COCO),":[181],"outperforming":[182],"models":[183],"fine-tuned":[184],"full":[187],"COCO":[188],"train":[189],"set.":[190]},"counts_by_year":[],"updated_date":"2026-03-27T14:29:43.386196","created_date":"2025-11-18T00:00:00"}
