{"id":"W4287120483","doi":"10.48550/arxiv.2106.06684","title":"Multistream ValidNet: Improving 6D Object Pose Estimation by Automatic\\n Multistream Validation","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Human Pose and Action Recognition","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Pose; Computer science; Artificial intelligence; Binary number; Classifier (UML); Pattern recognition (psychology); Estimation; Mathematics; 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002424501,0.0004186074,0.0003705757,0.0002850091,0.0003137765,0.0006256614,0.0008708735,0.000380565,0.0001730006],"category_scores_gemma":[0.0001050224,0.0005282938,0.0002500003,0.0004721695,0.00006205342,0.001245836,0.0008236801,0.0005205356,0.0001737341],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003789665,"about_ca_system_score_gemma":0.0002504165,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006551454,"about_ca_topic_score_gemma":0.00004649696,"domain_scores_codex":[0.9975201,0.0002596051,0.0003758159,0.001288953,0.0001858466,0.0003696403],"domain_scores_gemma":[0.9978119,0.0001176035,0.0005554355,0.001034658,0.0002852714,0.0001951365],"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":[0.000089955,0.003500831,0.002942658,0.00240389,0.001186648,0.00303316,0.005189029,0.4802056,0.009580094,0.01665788,0.009934624,0.4652757],"study_design_scores_gemma":[0.0009400875,0.00004987789,0.0006581339,0.0001832451,0.000112472,0.00002218415,0.0001410002,0.9839064,0.01155399,0.001794815,0.00006583035,0.0005719824],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4483153,0.00002689691,0.549547,0.00003086077,0.0006424011,0.0002924209,0.00004568154,0.0004105745,0.0006889026],"genre_scores_gemma":[0.9846905,0.00005250239,0.01292103,0.00006850118,0.00008614897,0.000004740878,0.0008429894,0.00002764103,0.001305953],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5366259,"threshold_uncertainty_score":0.9997169,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04067973349657065,"score_gpt":0.1959498262713466,"score_spread":0.1552700927747759,"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."}}