{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004314827,0.003417843,0.00189058,0.003099966,0.0007493487,0.002642778,0.004996544,0.002117159,0.007914271],"category_scores_gemma":[0.009813374,0.001222913,0.001277562,0.001201647,0.001177707,0.00358117,0.004100973,0.001772838,0.005119654],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001188015,"about_ca_system_score_gemma":0.001729127,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007836297,"about_ca_topic_score_gemma":0.01765259,"domain_scores_codex":[0.9960483,0.0006740869,0.0002118147,0.001022831,0.001731475,0.0003115206],"domain_scores_gemma":[0.9956452,0.001244709,0.0003445377,0.001533812,0.001059201,0.0001725465],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005890128,0.0003326496,0.006948363,0.0002255129,0.0002252451,0.0001676349,0.0001161378,0.05057109,0.02066301,0.002681426,0.01612611,0.9013538],"study_design_scores_gemma":[0.00004930408,0.0002299145,0.002429398,0.00006535007,0.00005012791,0.0002562849,0.00005644079,0.9586082,0.0270072,0.003782403,0.007413871,0.00005139429],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04768018,0.001117285,0.9088442,0.0002001285,0.000534645,0.0002537744,0.001109533,0.03498982,0.005270492],"genre_scores_gemma":[0.400371,0.0005365537,0.578301,0.00051965,0.0002159106,0.0002614094,0.00720745,0.002788526,0.009798421],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007914271,"threshold_uncertainty_score":0.02647591,"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."}}