{"id":"W4206330106","doi":"10.1109/avss52988.2021.9663785","title":"Deep Learning for Body Parts Detection using HRNet and EfficientNet","year":2021,"lang":"en","type":"article","venue":"","topic":"Human Pose and Action Recognition","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kelowna General Hospital; Université de Moncton","funders":"","keywords":"Benchmark (surveying); Computer science; Pose; Artificial intelligence; Set (abstract data type); Field (mathematics); Human body; Architecture; Machine learning; Pattern recognition (psychology); Computer vision; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009322786,0.00004758159,0.00005273446,0.00004037778,0.0001970343,0.0001585466,0.00003867391,0.00002760069,0.00003184891],"category_scores_gemma":[0.00002862187,0.00004752945,0.00002320634,0.0001045322,0.000008059808,0.0002109685,0.00003919072,0.00004898091,0.000009184064],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001165185,"about_ca_system_score_gemma":0.00001122546,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006578391,"about_ca_topic_score_gemma":0.00002272201,"domain_scores_codex":[0.9995267,0.00003010476,0.00007929398,0.0001895385,0.00006314166,0.0001111633],"domain_scores_gemma":[0.9997513,0.00004555294,0.00002940627,0.00007199702,0.00006616438,0.00003561057],"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":[0.00001433314,0.0001410596,0.0007782933,0.00008021585,0.00004389585,0.00002313926,0.001016508,0.01069069,0.08542968,0.006652305,0.0001413579,0.8949885],"study_design_scores_gemma":[0.0002001929,0.00004205986,0.0006960153,0.000007519627,0.000005999016,0.00004443368,0.00006162775,0.9121506,0.08138945,0.0006343734,0.004687172,0.0000805426],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2398667,0.00003696202,0.7591738,0.00003555732,0.0001410887,0.00004879991,9.108482e-8,0.000067126,0.0006298305],"genre_scores_gemma":[0.9893884,0.000009000893,0.01002556,0.0001271605,0.00005857744,0.000005061836,0.000003282965,0.000003564613,0.000379385],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9014599,"threshold_uncertainty_score":0.1938195,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02464309240257366,"score_gpt":0.261489224099053,"score_spread":0.2368461316964793,"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."}}