{"id":"W4394593131","doi":"10.1109/wacv57701.2024.00062","title":"Learning Better Keypoints for Multi-Object 6DoF Pose Estimation","year":2024,"lang":"en","type":"article","venue":"","topic":"Robot Manipulation and Learning","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Pose; Computer science; Artificial intelligence; Computer vision; Object (grammar); Estimation; Object detection; Pattern recognition (psychology); Engineering","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0001090085,0.00008535424,0.00007220568,0.00009594223,0.00005262152,0.0001015913,0.00003421126,0.00004666462,0.0003346655],"category_scores_gemma":[0.00004623156,0.00008087328,0.00005140947,0.00009312751,0.000004771166,0.0002203054,0.000007379556,0.0001470922,0.0003757903],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003185208,"about_ca_system_score_gemma":0.000004781162,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004792702,"about_ca_topic_score_gemma":0.000003474497,"domain_scores_codex":[0.9995524,0.00001131496,0.0001250553,0.0001134779,0.00006315833,0.0001345355],"domain_scores_gemma":[0.9998294,0.0000621424,0.000007045197,0.00005759276,0.0000144733,0.00002937629],"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":[0.000001407675,0.000002878798,0.0001864438,0.0001085836,0.00002043627,0.000001742794,0.0004195554,0.9451953,0.001487892,0.0004157277,0.001793713,0.05036634],"study_design_scores_gemma":[0.0001397975,0.00001561262,0.001312191,0.00003519263,0.000008589232,0.000002926781,0.00003963032,0.9875454,0.0006431382,0.00006301024,0.01008673,0.0001077928],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01915015,0.0001031581,0.9750337,0.0001320741,0.0004717961,0.0001407512,2.288233e-7,0.001243842,0.003724308],"genre_scores_gemma":[0.9427889,0.000003754008,0.05261331,0.00006047548,0.000124717,0.00002033713,0.00002600736,0.00004149555,0.004321007],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9236388,"threshold_uncertainty_score":0.483015,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03171944224868228,"score_gpt":0.2840819324616302,"score_spread":0.2523624902129479,"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."}}