{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002218704,0.0001055859,0.0001368834,0.0002002063,0.00007658426,0.00005730665,0.0001041761,0.00005628246,0.001286216],"category_scores_gemma":[0.00001432989,0.0001066951,0.00007831331,0.00035894,0.00001725786,0.0003493996,0.0000236868,0.0001487549,0.0000232867],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006435032,"about_ca_system_score_gemma":0.0001011196,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004598926,"about_ca_topic_score_gemma":0.00002385657,"domain_scores_codex":[0.9988624,0.00008224379,0.0002307435,0.0002727401,0.0004035343,0.000148317],"domain_scores_gemma":[0.9994557,0.00005472875,0.0001037183,0.0001698077,0.0001641124,0.0000519496],"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":[0.0004332841,0.002253811,0.001781279,0.0002166036,0.00003813519,0.0005807426,0.001185784,0.0008452031,0.2763555,0.00116194,0.0109388,0.7042089],"study_design_scores_gemma":[0.007137463,0.001245088,0.08319575,0.00138174,0.00006961863,0.00005747132,0.0005086507,0.07836215,0.8044685,0.007303995,0.01530534,0.0009642199],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7044557,0.00002198032,0.2825955,0.002789838,0.0004037831,0.0004091414,0.00009802105,0.0001493668,0.00907665],"genre_scores_gemma":[0.9877992,0.00001441783,0.0114171,0.0005081327,0.00006753034,0.00002027723,0.0001012367,0.000006893521,0.00006525252],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7032447,"threshold_uncertainty_score":0.9996268,"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."}}