{"id":"W4353100328","doi":"10.18280/ts.400112","title":"Scale and View Invariant Informative Joint Descriptor (SVI2JD) for Human Action Recognition from Skeleton Data","year":2023,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Human Pose and Action Recognition","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Action recognition; Skeleton (computer programming); Invariant (physics); Pattern recognition (psychology); Artificial intelligence; Joint (building); Scale (ratio); Computer science; Computer vision; Mathematics; Geography; Cartography; Engineering","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":[0.0006123004,0.0007775097,0.001041057,0.002260082,0.0002588291,0.000510461,0.001013954,0.0005544205,0.002451993],"category_scores_gemma":[0.00134244,0.000197353,0.0009457186,0.002456336,0.0004486706,0.0007070498,0.0008115043,0.0007185829,0.001303467],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005131385,"about_ca_system_score_gemma":0.001039787,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007328777,"about_ca_topic_score_gemma":0.009670641,"domain_scores_codex":[0.9992076,0.00007094488,0.00005624048,0.0002250937,0.0003523794,0.00008781971],"domain_scores_gemma":[0.9996102,0.00006085673,0.00005866918,0.00009436368,0.0001408868,0.0000351379],"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":[0.0003861098,0.0002075518,0.006263572,0.0002923546,0.0001618275,0.0001733699,0.00006986776,0.02524191,0.03466288,0.004727233,0.02662959,0.9011837],"study_design_scores_gemma":[0.00006415638,0.0004886967,0.02903906,0.00008030393,0.0001555873,0.0009583476,0.0001753099,0.895939,0.0344614,0.007963471,0.03054686,0.0001277247],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04480457,0.002345613,0.940743,0.0001950122,0.0003678085,0.000261149,0.003903409,0.003990047,0.003389395],"genre_scores_gemma":[0.5895572,0.002393168,0.3769133,0.0001991777,0.0002196137,0.0005307916,0.0215743,0.000298203,0.008314254],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007328777,"threshold_uncertainty_score":0.01457226,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1908223653481335,"score_gpt":0.3110662856661104,"score_spread":0.1202439203179769,"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."}}