{"id":"W3161500803","doi":"10.18280/ts.380233","title":"Dance Action Recognition and Pose Estimation Based on Deep Convolutional Neural Network","year":2021,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Human Pose and Action Recognition","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Convolutional neural network; Computer science; Artificial intelligence; Action (physics); Dance; Deep learning; Segmentation; Pose; Task (project management); Machine learning; Staring; Frame (networking); Sequence (biology); Pattern recognition (psychology); Engineering; Communication; Psychology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001932935,0.0001246128,0.0001003057,0.0000698102,0.000249073,0.0001694928,0.00008305682,0.00005058294,0.0004264385],"category_scores_gemma":[0.00001651448,0.0001338103,0.00004792,0.0002095872,0.00002725507,0.0005752823,0.00002464642,0.0001206707,0.00008132763],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005726031,"about_ca_system_score_gemma":0.0000477761,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003327731,"about_ca_topic_score_gemma":0.0000110545,"domain_scores_codex":[0.9988477,0.0001233611,0.0002116098,0.0003354331,0.0002877968,0.00019411],"domain_scores_gemma":[0.9994683,0.0001077709,0.00009812821,0.0001212724,0.0001273373,0.0000771912],"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":[0.000149798,0.0005231792,0.0005154667,0.00007101495,0.00004631384,0.00006203151,0.0002235479,0.08600456,0.005404586,0.005309659,0.003019216,0.8986706],"study_design_scores_gemma":[0.0008039079,0.0001563857,0.01107475,0.0000549047,0.00001538858,0.00003112011,0.00001363667,0.9769021,0.003341512,0.006964568,0.0004676584,0.0001740426],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2109599,0.00005283107,0.7863449,0.001056276,0.000501825,0.0001792054,0.00001140894,0.0001553231,0.0007383589],"genre_scores_gemma":[0.9839537,0.00001098623,0.01357852,0.001783894,0.0003517869,0.00003334983,0.0002466941,0.000007287287,0.00003383128],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8984966,"threshold_uncertainty_score":0.5456628,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04399115618748527,"score_gpt":0.252056106994896,"score_spread":0.2080649508074107,"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."}}