{"id":"https://openalex.org/W7167608034","doi":"https://doi.org/10.48550/arxiv.2607.03488","title":"Learning to Generate Multiple Objects from Dense and Occluded Layouts","display_name":"Learning to Generate Multiple Objects from Dense and Occluded Layouts","publication_year":2026,"publication_date":"2026-07-03","ids":{"openalex":"https://openalex.org/W7167608034","doi":"https://doi.org/10.48550/arxiv.2607.03488"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2607.03488","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.03488","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":null,"license_id":null,"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.2607.03488","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5102901293","display_name":"Hoang-Bach Ngo","orcid":"https://orcid.org/0009-0002-2290-1187"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ngo, Bach-Hoang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5082575514","display_name":"Son Tran Thai Ngo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ngo, Si-Tri","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5140198841","display_name":"Hieu Le","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Le, Hieu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5062359795","display_name":"Trung-Nghia Le","orcid":"https://orcid.org/0000-0002-7363-2610"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Le, Trung-Nghia","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":0.8998000025749207,"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":0.8998000025749207,"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":0.02630000002682209,"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/T12859","display_name":"Cell Image Analysis Techniques","score":0.009800000116229057,"subfield":{"id":"https://openalex.org/subfields/1304","display_name":"Biophysics"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/amodal-perception","display_name":"Amodal perception","score":0.7186999917030334},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.6218000054359436},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6011000275611877},{"id":"https://openalex.org/keywords/security-token","display_name":"Security token","score":0.5149000287055969},{"id":"https://openalex.org/keywords/filling-in","display_name":"Filling-in","score":0.4618000090122223},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.40689998865127563}],"concepts":[{"id":"https://openalex.org/C174478892","wikidata":"https://www.wikidata.org/wiki/Q4747455","display_name":"Amodal perception","level":3,"score":0.7186999917030334},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7124999761581421},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7107999920845032},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6686999797821045},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.6218000054359436},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6011000275611877},{"id":"https://openalex.org/C48145219","wikidata":"https://www.wikidata.org/wiki/Q1335365","display_name":"Security token","level":2,"score":0.5149000287055969},{"id":"https://openalex.org/C200873422","wikidata":"https://www.wikidata.org/wiki/Q5448821","display_name":"Filling-in","level":2,"score":0.4618000090122223},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.40689998865127563},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.33880001306533813},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3375999927520752},{"id":"https://openalex.org/C83248878","wikidata":"https://www.wikidata.org/wiki/Q344000","display_name":"Active appearance model","level":3,"score":0.2572000026702881},{"id":"https://openalex.org/C64876066","wikidata":"https://www.wikidata.org/wiki/Q5141226","display_name":"Cognitive neuroscience of visual object recognition","level":3,"score":0.257099986076355},{"id":"https://openalex.org/C147764199","wikidata":"https://www.wikidata.org/wiki/Q6865248","display_name":"Minification","level":2,"score":0.25189998745918274},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.2500999867916107}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2607.03488","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.03488","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2607.03488","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.03488","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":null,"license_id":null,"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":{"Text-to-image":[0],"diffusion":[1],"models":[2],"fail":[3],"to":[4,46],"generate":[5],"correct":[6],"object":[7],"counts":[8],"in":[9],"dense":[10],"scenes,":[11],"where":[12],"overlapping":[13,32,97],"instances":[14,41],"collapse":[15],"into":[16],"indistinguishable":[17],"structures":[18],"despite":[19],"appearing":[20],"visually":[21],"plausible.":[22],"We":[23,51],"identify":[24],"this":[25,53],"as":[26],"instance":[27,110],"ownership":[28],"collapse:":[29],"tokens":[30],"from":[31],"objects":[33,80],"interact":[34],"freely":[35],"through":[36,54],"attention,":[37],"while":[38,112],"heavily":[39],"occluded":[40,79],"receive":[42],"weak":[43],"supervision":[44],"due":[45],"their":[47,83],"small":[48],"visible":[49],"areas.":[50],"address":[52],"layout-aware":[55],"attention":[56],"biases":[57],"that":[58,75],"softly":[59],"bias":[60],"token":[61],"interactions":[62],"toward":[63],"region-consistent":[64],"grouping":[65],"and":[66,108],"suppress":[67],"cross-instance":[68],"leakage,":[69],"paired":[70],"with":[71,99],"an":[72],"amodal-balanced":[73],"loss":[74],"amplifies":[76],"gradients":[77],"for":[78],"based":[81],"on":[82],"occlusion":[84],"level.":[85],"To":[86],"enable":[87],"systematic":[88],"evaluation,":[89],"we":[90],"introduce":[91],"OverlapDepth-45K,":[92],"a":[93],"benchmark":[94],"of":[95],"densely":[96],"scenes":[98],"amodal":[100],"supervision.":[101],"Our":[102],"approach":[103],"substantially":[104],"improves":[105],"count":[106],"accuracy":[107],"prevents":[109],"merging":[111],"preserving":[113],"image":[114],"quality.":[115],"Project":[116],"page:":[117],"https://bachngoh.github.io/AIBL":[118]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-07-08T00:00:00"}