{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006512546,0.001421687,0.000894314,0.0008613593,0.00027024,0.000578552,0.001333171,0.0009468141,0.004995722],"category_scores_gemma":[0.00102628,0.0005498268,0.0006416723,0.0005955845,0.0003561139,0.0010137,0.0008899783,0.001076758,0.002406665],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006363856,"about_ca_system_score_gemma":0.0008131317,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008065455,"about_ca_topic_score_gemma":0.01335908,"domain_scores_codex":[0.9996785,0.00004133367,0.0000157808,0.0001255687,0.00007302205,0.00006578962],"domain_scores_gemma":[0.9997792,0.00005363366,0.00002552912,0.00005364829,0.00006743547,0.00002057117],"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":[0.0003884574,0.0002952081,0.002501547,0.0001800926,0.0001801521,0.0001731026,0.00005473079,0.1634139,0.02331305,0.003204981,0.0116603,0.7946345],"study_design_scores_gemma":[0.00001831833,0.0001359721,0.001247947,0.000020311,0.00003013919,0.0000717783,0.00001612522,0.9872183,0.00699588,0.002146424,0.002086313,0.00001258033],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05751903,0.001509748,0.922981,0.0002609573,0.0003128771,0.0001654011,0.0009092639,0.01233121,0.004010468],"genre_scores_gemma":[0.5901568,0.0009983948,0.387961,0.0006168899,0.0001526311,0.000361145,0.005120485,0.0004784714,0.01415417],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008065455,"threshold_uncertainty_score":0.01671237,"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."}}