{"id":"W4313563617","doi":"10.1145/3551349.3560433","title":"Consistent Scene Graph Generation by Constraint Optimization","year":2022,"lang":"en","type":"article","venue":"","topic":"Multimodal Machine Learning Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Scene graph; Local consistency; Consistency (knowledge bases); Constraint (computer-aided design); Graph; Probabilistic logic; Context (archaeology); Artificial intelligence; Theoretical computer science; Constraint satisfaction; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001702799,0.002081295,0.001180446,0.002453327,0.0008634359,0.00192197,0.003009551,0.001694724,0.005638662],"category_scores_gemma":[0.009344534,0.0008651591,0.001843378,0.002311984,0.001097965,0.0029391,0.002380769,0.002152375,0.001989892],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001604096,"about_ca_system_score_gemma":0.002261491,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01084082,"about_ca_topic_score_gemma":0.0221542,"domain_scores_codex":[0.9973747,0.0007007993,0.0001213341,0.0008069235,0.0008585783,0.0001376981],"domain_scores_gemma":[0.9949609,0.002388913,0.0003174475,0.001295327,0.0009105942,0.0001268909],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000256835,0.0002560378,0.002513176,0.000504185,0.000179323,0.0002582993,0.0001731015,0.6277435,0.01299425,0.02047236,0.02775842,0.3068905],"study_design_scores_gemma":[0.00003872665,0.00003107094,0.0003179535,0.00001322757,0.00002096007,0.00006980856,0.00004893416,0.970764,0.005860617,0.01877292,0.004043445,0.0000182269],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01621727,0.000252257,0.9713327,0.0003235186,0.00005075602,0.0002473105,0.001809112,0.007164869,0.002602175],"genre_scores_gemma":[0.1632451,0.0001963539,0.8190964,0.0003473056,0.00004046013,0.0003328431,0.01192682,0.002428221,0.002386393],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01084082,"threshold_uncertainty_score":0.02155542,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0160209572695437,"score_gpt":0.2410683267648134,"score_spread":0.2250473694952697,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}