{"id":"W4360989184","doi":"10.18280/ria.370123","title":"Action Recognition Using Segmental Action Network","year":2023,"lang":"fr","type":"article","venue":"Revue d intelligence artificielle","topic":"Human Pose and Action Recognition","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Action (physics); Computer science; Physics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001977514,0.0007948951,0.0005856543,0.0007342033,0.0001995378,0.0003848598,0.0006916499,0.0004820951,0.002626115],"category_scores_gemma":[0.0004361115,0.0001961219,0.0005138414,0.0006173868,0.000304837,0.0005928357,0.0004904736,0.0004844781,0.0008108926],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006273976,"about_ca_system_score_gemma":0.0005351658,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01358058,"about_ca_topic_score_gemma":0.01758176,"domain_scores_codex":[0.9997832,0.00002173831,0.00000936638,0.0001014672,0.00004793316,0.00003625079],"domain_scores_gemma":[0.9998823,0.00002514728,0.00001900452,0.00002051876,0.00003860937,0.0000143964],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004090828,0.0002258508,0.004686587,0.0000970165,0.0001420722,0.000280363,0.0000590743,0.2500108,0.04669032,0.003544024,0.006891866,0.6869628],"study_design_scores_gemma":[0.000003981805,0.00006857659,0.002690153,0.000009135082,0.00002405337,0.00007053133,0.00001513822,0.9861103,0.007625267,0.001759422,0.001613114,0.00001032587],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1410015,0.001434757,0.8400149,0.0003059141,0.0002383077,0.0001592442,0.001908911,0.004399619,0.01053684],"genre_scores_gemma":[0.8849479,0.0008031047,0.1011047,0.0001936773,0.00007074048,0.0001200642,0.003349223,0.00007470301,0.009335794],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01358058,"threshold_uncertainty_score":0.02700311,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.263904700676882,"score_gpt":0.3513847683628575,"score_spread":0.08748006768597555,"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."}}