{"id":"W4200304330","doi":"10.23919/iccas52745.2021.9649799","title":"Viewpoint Selection for DermDrone using Deep Reinforcement Learning","year":2021,"lang":"en","type":"article","venue":"2021 21st International Conference on Control, Automation and Systems (ICCAS)","topic":"Human Pose and Action Recognition","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Mitacs","keywords":"Computer science; Reinforcement learning; Pose; Artificial intelligence; Trajectory; Key (lock); Selection (genetic algorithm); Computer vision; 3D pose estimation; Monocular; Machine learning","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.0004135302,0.00017215,0.0002444496,0.0001939556,0.0003034779,0.0007249197,0.0001699694,0.00009229018,0.0003068715],"category_scores_gemma":[0.00013161,0.0001762479,0.00008642867,0.0001460459,0.00001749736,0.0006041749,0.00004370688,0.0001514346,0.000058273],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001609152,"about_ca_system_score_gemma":0.0001412463,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002881302,"about_ca_topic_score_gemma":0.00003407137,"domain_scores_codex":[0.998325,0.0001874971,0.0004840811,0.0004118467,0.0003915947,0.0001999399],"domain_scores_gemma":[0.9984863,0.0001142641,0.0003065362,0.0001380414,0.0008688103,0.00008605369],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005061711,0.0001071998,0.0002419055,0.0001059198,0.0002333723,0.000009903279,0.0005964038,0.033041,0.03186275,0.8913133,0.0001913726,0.04224622],"study_design_scores_gemma":[0.001238211,0.0000974949,0.0002413088,0.0001569125,0.00001928984,0.0000916089,0.0002862773,0.9917277,0.001279896,0.0009377581,0.003733032,0.0001905788],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01209261,0.00008815664,0.97763,0.001761014,0.001275601,0.0004112972,0.000005346867,0.0001296311,0.00660634],"genre_scores_gemma":[0.9961737,0.00008885528,0.00118028,0.0005294696,0.0002778793,0.0001043283,0.00008136389,0.0000107817,0.001553329],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9840811,"threshold_uncertainty_score":0.7187181,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05108417350775919,"score_gpt":0.2973842515735463,"score_spread":0.2463000780657871,"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."}}