{"id":"W4386243184","doi":"10.1109/crv60082.2023.00024","title":"Transformer-Based Human Action Recognition with Dynamic Feature Selection","year":2023,"lang":"en","type":"article","venue":"","topic":"Human Pose and Action Recognition","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Discriminative model; Action recognition; Artificial intelligence; Transformer; Encoder; Leverage (statistics); Machine learning; Pattern recognition (psychology); Human motion; Feature selection; Computer vision; Motion (physics); Engineering","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002646047,0.0006033053,0.0006506885,0.0004494446,0.0001279573,0.0003211734,0.0008337522,0.000316544,0.00149682],"category_scores_gemma":[0.0006920218,0.0002217276,0.0005521238,0.0004201881,0.0004188716,0.0006202981,0.0005530661,0.0005161291,0.0005969017],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004162551,"about_ca_system_score_gemma":0.0004224151,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004054611,"about_ca_topic_score_gemma":0.005863976,"domain_scores_codex":[0.9998001,0.00002402629,0.000006634034,0.00009209746,0.00004437859,0.00003270965],"domain_scores_gemma":[0.9998536,0.00004303631,0.00002132005,0.00002788303,0.00003056986,0.00002364972],"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":[0.0005059225,0.0002400235,0.002828233,0.00007733147,0.00009125954,0.0002381046,0.00007853541,0.1465998,0.07833125,0.004859175,0.005058378,0.7610919],"study_design_scores_gemma":[0.000009526636,0.00008629492,0.0008460807,0.000003681117,0.00001630724,0.0001129411,0.0000106941,0.9856498,0.009687758,0.002887239,0.0006808162,0.000008932239],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.06743626,0.0004224672,0.9270409,0.0001437238,0.00006536395,0.00006158038,0.0002477608,0.002535181,0.002046689],"genre_scores_gemma":[0.9084895,0.0003217847,0.08680589,0.0001641377,0.00003978985,0.00005648701,0.0005593671,0.00008536535,0.003477641],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004054611,"threshold_uncertainty_score":0.008062005,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03423737450805013,"score_gpt":0.285123491204596,"score_spread":0.2508861166965459,"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."}}