{"id":"W4386245243","doi":"10.1109/crv60082.2023.00035","title":"HyperMODEST: Self-Supervised 3D Object Detection with Confidence Score Filtering","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Object detection; Artificial intelligence; Object (grammar); Precision and recall; Code (set theory); Process (computing); Filter (signal processing); Recall; Range (aeronautics); Baseline (sea); Low Confidence; Detector; F1 score; Machine learning; Pattern recognition (psychology); Computer vision","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":[0.001861539,0.002177234,0.001618028,0.001969317,0.0006319447,0.001570258,0.006688269,0.002224711,0.005456968],"category_scores_gemma":[0.005818368,0.001032616,0.001539111,0.001529445,0.001020419,0.002228429,0.003236458,0.002533742,0.003956473],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00117812,"about_ca_system_score_gemma":0.001556333,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0126137,"about_ca_topic_score_gemma":0.02566713,"domain_scores_codex":[0.9984584,0.0001659689,0.00005863031,0.0006205377,0.0005592802,0.0001371582],"domain_scores_gemma":[0.9979731,0.0005174996,0.0001287686,0.0007585957,0.000515896,0.0001061507],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004598425,0.0006601491,0.007164191,0.0003212323,0.0003599052,0.0001514179,0.000159684,0.1269958,0.02292175,0.002906994,0.05584982,0.7820492],"study_design_scores_gemma":[0.00005432308,0.00009727547,0.00110882,0.00002010865,0.00002039888,0.00009852713,0.0000249456,0.9800048,0.01158477,0.002577413,0.004378109,0.00003043253],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0433052,0.0006943145,0.879778,0.0003251107,0.0002843727,0.0003946071,0.002688519,0.06870549,0.003824279],"genre_scores_gemma":[0.2999978,0.0002446117,0.6737329,0.0006840017,0.000123625,0.0005863769,0.01373466,0.002495951,0.008400143],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0126137,"threshold_uncertainty_score":0.02508056,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02601647411069827,"score_gpt":0.2462329755890092,"score_spread":0.2202165014783109,"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."}}