{"id":"W2789177853","doi":"10.1609/aaai.v32i1.12274","title":"Visual Relationship Detection With Deep Structural Ranking","year":2018,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Multimodal Machine Learning Applications","field":"Computer Science","cited_by":96,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"China Scholarship Council; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; Canada Research Chairs","keywords":"Ranking (information retrieval); Computer science; Artificial intelligence; Complement (music); Representation (politics); Relevance (law); Object (grammar); Machine learning; Pattern recognition (psychology); Function (biology); Image (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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003300525,0.0001734391,0.0001472843,0.0001297704,0.000525368,0.0002409745,0.001234201,0.00007083367,0.00003629797],"category_scores_gemma":[0.0003923741,0.0001217028,0.00005396737,0.0008920235,0.000351119,0.0004102221,0.0002062249,0.0003509525,0.0001062049],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000466385,"about_ca_system_score_gemma":0.0000512651,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001194435,"about_ca_topic_score_gemma":0.00006991549,"domain_scores_codex":[0.9985198,0.00002252535,0.0003368224,0.0004463032,0.0004187365,0.0002557656],"domain_scores_gemma":[0.9984748,0.0001220671,0.0003450099,0.0002764773,0.0007171959,0.00006441605],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007017404,0.00003736128,0.006326217,0.0000135666,0.00001066862,1.02758e-7,0.001682347,0.0001380551,0.03518647,0.8257719,0.000002928281,0.1307602],"study_design_scores_gemma":[0.00003353711,0.000329199,0.02537529,0.00007713203,0.00001097316,0.00001023555,0.0001673112,0.5554467,0.3068952,0.1114423,0.00001681365,0.0001953078],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7075831,0.000003385019,0.2861,0.001190766,0.0001736593,0.0003075876,5.225205e-7,0.0001489172,0.004492107],"genre_scores_gemma":[0.9887968,7.539192e-7,0.01091812,0.00008588975,0.0001138055,0.00002940811,2.606652e-7,0.0000118541,0.00004307351],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7143297,"threshold_uncertainty_score":0.4962895,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04774393774670666,"score_gpt":0.3167447715831568,"score_spread":0.2690008338364502,"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."}}