{"id":"W4223903815","doi":"10.1016/j.patrec.2022.04.008","title":"The impact of domain randomization on cross-device monocular deep 6DoF detection","year":2022,"lang":"en","type":"article","venue":"Pattern Recognition Letters","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université Laval; Université de Montréal","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico","keywords":"Computer science; Monocular; Artificial intelligence; RGB color model; Synthetic data; Computer vision; Domain (mathematical analysis); Deep learning; 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":[0.001253893,0.0006832708,0.0005918973,0.0002828775,0.0005090609,0.0007447387,0.0006942255,0.0009459096,0.005050101],"category_scores_gemma":[0.01041029,0.0002340519,0.0002356302,0.0003779575,0.000556997,0.001815018,0.001272921,0.0008046446,0.001176904],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004082657,"about_ca_system_score_gemma":0.001061047,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001604489,"about_ca_topic_score_gemma":0.002518007,"domain_scores_codex":[0.9989715,0.0003499591,0.00004149125,0.0002184006,0.0002334779,0.0001852641],"domain_scores_gemma":[0.9958879,0.002418673,0.0002037509,0.0009176819,0.000422695,0.0001493303],"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":[0.004382992,0.0006228002,0.006022874,0.0004464954,0.0001562975,0.0003171931,0.0001283814,0.2512388,0.1938126,0.03676616,0.009142452,0.496963],"study_design_scores_gemma":[0.00007538295,0.0004828364,0.003245683,0.00005742155,0.00004323309,0.0004452987,0.00009548866,0.8833247,0.09719507,0.01141207,0.003573475,0.00004936305],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.296444,0.00186789,0.6826601,0.001196697,0.0003849325,0.00008318448,0.0006212516,0.002499936,0.01424199],"genre_scores_gemma":[0.9273221,0.0003392587,0.0687089,0.0003231786,0.00004650655,0.00004362749,0.0004465431,0.0001697298,0.00260019],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005050101,"threshold_uncertainty_score":0.01689422,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01068569763585923,"score_gpt":0.2319034864576213,"score_spread":0.2212177888217621,"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."}}