{"id":"https://openalex.org/W7155225226","doi":"https://doi.org/10.48550/arxiv.2604.18623","title":"Can We Build Scene Graphs, Not Classify Them? FlowSG: Progressive Image-Conditioned Scene Graph Generation with Flow Matching","display_name":"Can We Build Scene Graphs, Not Classify Them? FlowSG: Progressive Image-Conditioned Scene Graph Generation with Flow Matching","publication_year":2026,"publication_date":"2026-04-18","ids":{"openalex":"https://openalex.org/W7155225226","doi":"https://doi.org/10.48550/arxiv.2604.18623"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.18623","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.18623","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":false,"raw_source_name":null,"raw_type":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2604.18623","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5134286911","display_name":"Xin Hu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hu, Xin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134242349","display_name":"Ke Qin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Qin, Ke","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134294716","display_name":"Wen Yin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yin, Wen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017943466","display_name":"Yuan-Fang Li","orcid":"https://orcid.org/0000-0003-4651-2821"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Yuan-Fang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134223436","display_name":"Ming Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Ming","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5134305574","display_name":"Tao He","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"He, Tao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":6,"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":0.9674999713897705,"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":0.9674999713897705,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.01360000018030405,"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":0.004699999932199717,"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/scene-graph","display_name":"Scene graph","score":0.5357999801635742},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.49970000982284546},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.44440001249313354},{"id":"https://openalex.org/keywords/conditional-random-field","display_name":"Conditional random field","score":0.4255000054836273},{"id":"https://openalex.org/keywords/categorical-variable","display_name":"Categorical variable","score":0.39340001344680786},{"id":"https://openalex.org/keywords/eulerian-path","display_name":"Eulerian path","score":0.38449999690055847},{"id":"https://openalex.org/keywords/predicate","display_name":"Predicate (mathematical logic)","score":0.3833000063896179},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3822999894618988}],"concepts":[{"id":"https://openalex.org/C179372163","wikidata":"https://www.wikidata.org/wiki/Q1406181","display_name":"Scene graph","level":3,"score":0.5357999801635742},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5288000106811523},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5202000141143799},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.49970000982284546},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.44440001249313354},{"id":"https://openalex.org/C152565575","wikidata":"https://www.wikidata.org/wiki/Q1124538","display_name":"Conditional random field","level":2,"score":0.4255000054836273},{"id":"https://openalex.org/C5274069","wikidata":"https://www.wikidata.org/wiki/Q2285707","display_name":"Categorical variable","level":2,"score":0.39340001344680786},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.38510000705718994},{"id":"https://openalex.org/C43058520","wikidata":"https://www.wikidata.org/wiki/Q624580","display_name":"Eulerian path","level":3,"score":0.38449999690055847},{"id":"https://openalex.org/C140146324","wikidata":"https://www.wikidata.org/wiki/Q1144319","display_name":"Predicate (mathematical logic)","level":2,"score":0.3833000063896179},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3822999894618988},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.3815999925136566},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.36739999055862427},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.3659999966621399},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3626999855041504},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3402000069618225},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.33500000834465027},{"id":"https://openalex.org/C128107574","wikidata":"https://www.wikidata.org/wiki/Q182003","display_name":"Injective function","level":2,"score":0.33239999413490295},{"id":"https://openalex.org/C22367795","wikidata":"https://www.wikidata.org/wiki/Q7625208","display_name":"Structured prediction","level":2,"score":0.3244999945163727},{"id":"https://openalex.org/C129916263","wikidata":"https://www.wikidata.org/wiki/Q1141183","display_name":"Backward chaining","level":4,"score":0.3122999966144562},{"id":"https://openalex.org/C43711488","wikidata":"https://www.wikidata.org/wiki/Q7534783","display_name":"Skew","level":2,"score":0.3077000081539154},{"id":"https://openalex.org/C155542232","wikidata":"https://www.wikidata.org/wiki/Q736111","display_name":"Optical flow","level":3,"score":0.2694999873638153},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.26669999957084656},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.25940001010894775},{"id":"https://openalex.org/C64876066","wikidata":"https://www.wikidata.org/wiki/Q5141226","display_name":"Cognitive neuroscience of visual object recognition","level":3,"score":0.258899986743927},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.25270000100135803},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2502000033855438}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.18623","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.18623","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":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.18623","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.18623","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":false,"raw_source_name":null,"raw_type":"article"},"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":{"Scene":[0],"Graph":[1],"Generation":[2],"(SGG)":[3],"unifies":[4],"object":[5],"localization":[6],"and":[7,14,70,107,117,122,143,149,155,159,168],"visual":[8,86],"relationship":[9],"reasoning":[10],"by":[11],"predicting":[12],"boxes":[13],"subject-predicate-object":[15],"triples.":[16],"Yet":[17],"most":[18],"pipelines":[19],"treat":[20],"SGG":[21,40],"as":[22,41],"a":[23,30,45,51,77,81,92,98,135],"one-shot,":[24],"deterministic":[25],"classification":[26,179],"problem":[27],"rather":[28],"than":[29],"genuinely":[31],"progressive,":[32],"generative":[33,175],"task.":[34],"We":[35],"propose":[36],"FlowSG,":[37],"which":[38],"recasts":[39],"continuous-time":[42],"transport":[43,103],"on":[44,153],"hybrid":[46],"discrete-continuous":[47,174],"state:":[48],"starting":[49],"from":[50],"noised":[52],"graph,":[53],"the":[54,172,190],"model":[55],"progressively":[56],"grows":[57],"an":[58,182],"image-conditioned":[59],"scene":[60,82],"graph":[61,83,93],"through":[62],"constraint-aware":[63],"refinements":[64],"that":[65],"jointly":[66],"synthesize":[67],"nodes":[68],"(objects)":[69],"edges":[71],"(predicates).":[72],"Specifically,":[73],"we":[74],"first":[75],"leverage":[76],"VQ-VAE":[78],"to":[79,102],"quantize":[80],"(e.g.,":[84],"continuous":[85,104],"features)":[87],"into":[88],"compact,":[89],"predictable":[90],"tokens;":[91],"Transformer":[94],"then":[95],"(i)":[96],"predicts":[97],"conditional":[99],"velocity":[100],"field":[101],"geometry":[105,123,133],"(boxes)":[106],"(ii)":[108],"updates":[109],"discrete":[110],"posteriors":[111],"for":[112,132,138],"categorical":[113],"tokens":[114],"(object":[115],"features":[116],"predicate":[118,166],"labels),":[119],"coupling":[120],"semantics":[121],"via":[124],"flow-conditioned":[125],"message":[126],"aggregation.":[127],"Training":[128],"combines":[129],"flow-matching":[130],"losses":[131],"with":[134,146,181],"discrete-flow":[136],"objective":[137],"tokens,":[139],"yielding":[140],"few-step":[141],"inference":[142],"plug-and-play":[144],"compatibility":[145],"standard":[147],"detectors":[148],"segmenters.":[150],"Extensive":[151],"experiments":[152],"VG":[154],"PSG":[156],"under":[157],"closed-":[158],"open-vocabulary":[160],"protocols":[161],"show":[162],"consistent":[163],"gains":[164],"in":[165],"R/mR":[167],"graph-level":[169],"metrics,":[170],"validating":[171],"mixed":[173],"formulation":[176],"over":[177,189],"one-shot":[178],"baselines,":[180],"average":[183],"improvement":[184],"of":[185],"about":[186],"3":[187],"points":[188],"state-of-the-art":[191],"USG-Par.":[192]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-04-23T00:00:00"}