{"id":"https://openalex.org/W4416830869","doi":"https://doi.org/10.48550/arxiv.2509.22383","title":"GPT-4 for Occlusion Order Recovery","display_name":"GPT-4 for Occlusion Order Recovery","publication_year":2025,"publication_date":"2025-09-26","ids":{"openalex":"https://openalex.org/W4416830869","doi":"https://doi.org/10.48550/arxiv.2509.22383"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2509.22383","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2509.22383","pdf_url":"https://arxiv.org/pdf/2509.22383","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":null,"license_id":null,"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/2509.22383","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5079064995","display_name":"Kaziwa Saleh","orcid":"https://orcid.org/0000-0003-3902-1063"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Saleh, Kaziwa","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5120440333","display_name":"Zhyar Rzgar K Rostam","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Rostam, Zhyar Rzgar K","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5024487424","display_name":"S\u00e1ndor Sz\u00e9n\u00e1si","orcid":"https://orcid.org/0000-0002-7292-0717"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sz\u00e9n\u00e1si, S\u00e1ndor","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5052074421","display_name":"Zolt\u00e1n V\u00e1mossy","orcid":"https://orcid.org/0000-0002-6040-9954"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"V\u00e1mossy, Zolt\u00e1n","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":0.6549000144004822,"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":0.6549000144004822,"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":0.08429999649524689,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.06930000334978104,"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/occlusion","display_name":"Occlusion","score":0.8519999980926514},{"id":"https://openalex.org/keywords/parsing","display_name":"Parsing","score":0.4510999917984009},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.39010000228881836},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.36739999055862427},{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.3343000113964081},{"id":"https://openalex.org/keywords/matrix","display_name":"Matrix (chemical analysis)","score":0.3260999917984009}],"concepts":[{"id":"https://openalex.org/C2776268601","wikidata":"https://www.wikidata.org/wiki/Q968808","display_name":"Occlusion","level":2,"score":0.8519999980926514},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6775000095367432},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6395999789237976},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5817000269889832},{"id":"https://openalex.org/C186644900","wikidata":"https://www.wikidata.org/wiki/Q194152","display_name":"Parsing","level":2,"score":0.4510999917984009},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.39010000228881836},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.36739999055862427},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.3343000113964081},{"id":"https://openalex.org/C106487976","wikidata":"https://www.wikidata.org/wiki/Q685816","display_name":"Matrix (chemical analysis)","level":2,"score":0.3260999917984009},{"id":"https://openalex.org/C55020928","wikidata":"https://www.wikidata.org/wiki/Q3813865","display_name":"Image quality","level":3,"score":0.3077000081539154},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.3037000000476837},{"id":"https://openalex.org/C182306322","wikidata":"https://www.wikidata.org/wiki/Q1779371","display_name":"Order (exchange)","level":2,"score":0.30090001225471497},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.29919999837875366},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.289900004863739},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.2718000113964081}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2509.22383","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2509.22383","pdf_url":"https://arxiv.org/pdf/2509.22383","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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2509.22383","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2509.22383","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2509.22383","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2509.22383","pdf_url":"https://arxiv.org/pdf/2509.22383","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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"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":{"Occlusion":[0],"remains":[1],"a":[2,42,52,140],"significant":[3],"challenge":[4],"for":[5],"current":[6],"vision":[7],"models":[8],"to":[9,24,46,76],"robustly":[10],"interpret":[11],"complex":[12],"and":[13,17,23,66,92,105,118,149],"dense":[14],"real-world":[15],"images":[16],"scenes.":[18],"To":[19],"address":[20],"this":[21],"limitation":[22],"enable":[25],"accurate":[26,126],"prediction":[27],"of":[28,41,99],"the":[29,38,48,58,64,97,101,121,132],"occlusion":[30,79,89,137,155],"order":[31,68,127],"relationship":[32],"between":[33],"objects,":[34],"we":[35],"propose":[36],"leveraging":[37],"advanced":[39],"capability":[40],"pre-trained":[43],"GPT-4":[44,61],"model":[45,102,122,133],"deduce":[47],"order.":[49],"By":[50],"providing":[51],"specifically":[53],"designed":[54],"prompt":[55],"along":[56],"with":[57,87],"input":[59],"image,":[60],"can":[62,72,82,123,134,150],"analyze":[63],"image":[65,93],"generate":[67],"predictions.":[69,128],"The":[70,108],"response":[71],"then":[73],"be":[74,83,152],"parsed":[75],"construct":[77],"an":[78],"matrix":[80],"which":[81,143],"utilized":[84],"in":[85,139],"assisting":[86],"other":[88],"handling":[90,156],"tasks":[91],"understanding.":[94],"We":[95],"report":[96],"results":[98,109],"evaluating":[100],"on":[103],"COCOA":[104],"InstaOrder":[106],"datasets.":[107],"show":[110],"that":[111],"by":[112],"using":[113],"semantic":[114],"context,":[115],"visual":[116],"patterns,":[117],"commonsense":[119],"knowledge,":[120],"produce":[124],"more":[125],"Unlike":[129],"baseline":[130],"methods,":[131],"reason":[135],"about":[136],"relationships":[138],"zero-shot":[141],"fashion,":[142],"requires":[144],"no":[145],"annotated":[146],"training":[147],"data":[148],"easily":[151],"integrated":[153],"into":[154],"frameworks.":[157]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2025-10-10T00:00:00"}
