{"id":"W4403888467","doi":"10.1007/978-3-031-73039-9_26","title":"Domain Generalization of 3D Object Detection by Density-Resampling","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Generalization; Resampling; Domain (mathematical analysis); Artificial intelligence; Object (grammar); Computer vision; Pattern recognition (psychology); Mathematics","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":[0.0009810667,0.0006095942,0.00108315,0.001090074,0.0002496071,0.0006709117,0.001184181,0.000823795,0.002259127],"category_scores_gemma":[0.002522765,0.0005174305,0.001358198,0.0008784613,0.0006200268,0.0008225071,0.001343842,0.0009599522,0.0008859918],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006245667,"about_ca_system_score_gemma":0.0005332908,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004186776,"about_ca_topic_score_gemma":0.003625769,"domain_scores_codex":[0.9995602,0.000112479,0.0000181708,0.0001092121,0.000158207,0.00004172464],"domain_scores_gemma":[0.9990808,0.0003703177,0.00006461557,0.000258758,0.0001869781,0.00003850906],"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":[0.0002659144,0.0001439343,0.001286433,0.0003579673,0.0001750169,0.0001860479,0.00016544,0.3200899,0.06332711,0.04091696,0.006301052,0.5667841],"study_design_scores_gemma":[0.000003849521,0.00002023264,0.0005085353,0.000006198416,0.000009519382,0.0001101123,0.000008107096,0.9860514,0.003749527,0.008250624,0.001269966,0.00001184623],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01018912,0.0002391106,0.9876257,0.0000782958,0.00003828916,0.00002680685,0.0000742404,0.0004745786,0.001253817],"genre_scores_gemma":[0.3191458,0.0008589769,0.6731369,0.0002592968,0.0001432941,0.0001003711,0.0007334345,0.0002954304,0.005326565],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004186776,"threshold_uncertainty_score":0.008324802,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0134771364041977,"score_gpt":0.2518982230917263,"score_spread":0.2384210866875286,"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."}}