{"id":"https://openalex.org/W7160040555","doi":"https://doi.org/10.48550/arxiv.2605.00273","title":"When Do Diffusion Models learn to Generate Multiple Objects?","display_name":"When Do Diffusion Models learn to Generate Multiple Objects?","publication_year":2026,"publication_date":"2026-04-30","ids":{"openalex":"https://openalex.org/W7160040555","doi":"https://doi.org/10.48550/arxiv.2605.00273"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.00273","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.00273","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.2605.00273","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5135224393","display_name":"Yujin Jeong","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jeong, Yujin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135216848","display_name":"Arnas Uselis","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Uselis, Arnas","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5062808708","display_name":"Iro Laina","orcid":"https://orcid.org/0000-0001-8857-7709"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Laina, Iro","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135149029","display_name":"Seong Joon Oh","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Oh, Seong Joon","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5037747070","display_name":"Anna Rohrbach","orcid":"https://orcid.org/0000-0003-1161-6006"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Rohrbach, Anna","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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.2888999879360199,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.2888999879360199,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.19920000433921814,"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":0.15459999442100525,"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/generalization","display_name":"Generalization","score":0.694100022315979},{"id":"https://openalex.org/keywords/diffusion","display_name":"Diffusion","score":0.5309000015258789},{"id":"https://openalex.org/keywords/perspective","display_name":"Perspective (graphical)","score":0.3569999933242798},{"id":"https://openalex.org/keywords/contrast","display_name":"Contrast (vision)","score":0.33660000562667847},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.31290000677108765},{"id":"https://openalex.org/keywords/empirical-research","display_name":"Empirical research","score":0.29789999127388}],"concepts":[{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.694100022315979},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6154999732971191},{"id":"https://openalex.org/C69357855","wikidata":"https://www.wikidata.org/wiki/Q163214","display_name":"Diffusion","level":2,"score":0.5309000015258789},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.46889999508857727},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.38600000739097595},{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.3569999933242798},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.33660000562667847},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3271999955177307},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.31290000677108765},{"id":"https://openalex.org/C120936955","wikidata":"https://www.wikidata.org/wiki/Q2155640","display_name":"Empirical research","level":2,"score":0.29789999127388},{"id":"https://openalex.org/C66024118","wikidata":"https://www.wikidata.org/wiki/Q1122506","display_name":"Computational model","level":2,"score":0.2752000093460083},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2734000086784363},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.27160000801086426},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.2614000141620636},{"id":"https://openalex.org/C166052673","wikidata":"https://www.wikidata.org/wiki/Q83021","display_name":"Empirical evidence","level":2,"score":0.25940001010894775},{"id":"https://openalex.org/C527412718","wikidata":"https://www.wikidata.org/wiki/Q855395","display_name":"Interpretation (philosophy)","level":2,"score":0.2517000138759613}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.00273","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.00273","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.2605.00273","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.00273","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":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Text-to-image":[0],"diffusion":[1,101,148],"models":[2,102,149],"achieve":[3],"impressive":[4],"visual":[5],"fidelity,":[6],"yet":[7],"they":[8],"remain":[9,24],"unreliable":[10],"in":[11,126],"multi-object":[12,160],"generation.":[13,98,162],"Despite":[14],"extensive":[15],"empirical":[16],"evidence":[17],"of":[18,32,75,147],"these":[19,83],"failures,":[20],"the":[21,37],"underlying":[22],"causes":[23],"unclear.":[25],"We":[26],"begin":[27],"by":[28],"asking":[29],"how":[30],"much":[31],"this":[33],"limitation":[34],"arises":[35],"from":[36],"data":[38,42,66,156],"itself.":[39],"To":[40,81],"disentangle":[41],"effects,":[43],"we":[44,85,105],"consider":[45],"two":[46],"regimes":[47],"across":[48],"different":[49],"dataset":[50,97],"sizes:":[51],"(1)":[52],"concept":[53,58,116,135],"generalization,":[54,71],"where":[55,72],"each":[56],"individual":[57],"is":[59,121],"observed":[60],"during":[61,140],"training":[62,100],"under":[63],"potentially":[64],"imbalanced":[65],"distributions,":[67],"and":[68,118,150,155],"(2)":[69],"compositional":[70,130,161],"specific":[73],"combinations":[74,136],"concepts":[76],"are":[77,137],"systematically":[78],"held":[79,138],"out.":[80],"study":[82],"regimes,":[84],"introduce":[86],"mosaic":[87],"(Multi-Object":[88],"Spatial":[89],"relations,":[90],"AttrIbution,":[91],"Counting),":[92],"a":[93,111],"controlled":[94],"framework":[95],"for":[96,158],"By":[99],"on":[103],"mosaic,":[104],"find":[106],"that":[107,119],"scene":[108],"complexity":[109],"plays":[110],"dominant":[112],"role":[113],"rather":[114],"than":[115],"imbalance,":[117],"counting":[120],"uniquely":[122],"difficult":[123],"to":[124],"learn":[125],"low-data":[127],"regimes.":[128],"Moreover,":[129],"generalization":[131],"collapses":[132],"as":[133],"more":[134],"out":[139],"training.":[141],"These":[142],"findings":[143],"highlight":[144],"fundamental":[145],"limitations":[146],"motivate":[151],"stronger":[152],"inductive":[153],"biases":[154],"design":[157],"robust":[159]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-05-05T00:00:00"}