{"id":"W4206502914","doi":"10.18280/ts.380624","title":"Image Recognition of Standard Actions in Sports Videos Based on Feature Fusion","year":2021,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Human Pose and Action Recognition","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Artificial intelligence; Feature (linguistics); Action (physics); Feature extraction; Granularity; Pattern recognition (psychology); Feature vector; Action recognition; Key (lock); Machine learning; Computer vision; Class (philosophy)","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":[0.0003592303,0.0005471407,0.0005987201,0.001598014,0.0001818953,0.000455017,0.000440997,0.0004232918,0.001152065],"category_scores_gemma":[0.0008037738,0.0001550032,0.0005409897,0.000871977,0.0003125074,0.0007107513,0.0004719794,0.0003755705,0.0004704001],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002533109,"about_ca_system_score_gemma":0.0002976811,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00219184,"about_ca_topic_score_gemma":0.002598906,"domain_scores_codex":[0.999652,0.00003007952,0.00001892365,0.0001035828,0.000136094,0.00005921142],"domain_scores_gemma":[0.9997361,0.00004970829,0.00003492982,0.00004073441,0.000109226,0.00002929371],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003617893,0.0001177611,0.002730695,0.0001016003,0.00006145513,0.0001939944,0.000091657,0.009347463,0.2087207,0.001346881,0.001534549,0.7753915],"study_design_scores_gemma":[0.00002964432,0.0005480417,0.03657296,0.00003499293,0.0001423628,0.00123216,0.0002264556,0.7200875,0.2316362,0.003297476,0.006111235,0.00008097379],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1187233,0.0004604894,0.8759075,0.00008188104,0.00009773409,0.0001235369,0.0002051736,0.001733833,0.00266657],"genre_scores_gemma":[0.661333,0.000534095,0.3346869,0.000101637,0.00008932129,0.00008814724,0.0005731921,0.0000908927,0.002502784],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00219184,"threshold_uncertainty_score":0.004358172,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02248242276760292,"score_gpt":0.2541580564818104,"score_spread":0.2316756337142075,"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."}}