{"id":"W4403791396","doi":"10.1145/3664647.3688975","title":"Advancing Micro-Action Recognition with Multi-Auxiliary Heads and Hybrid Loss Optimization","year":2024,"lang":"en","type":"article","venue":"","topic":"Human Pose and Action Recognition","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University; Mila - Quebec Artificial Intelligence Institute","funders":"","keywords":"Computer science; Action (physics); Action recognition; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"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":[0.00145453,0.001807262,0.001524124,0.0007547824,0.0003983253,0.001094232,0.002152026,0.001315628,0.002884746],"category_scores_gemma":[0.002287342,0.0005104029,0.00134651,0.0005667431,0.0007989621,0.001812415,0.001954553,0.002690828,0.001978268],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001033865,"about_ca_system_score_gemma":0.00129595,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006396461,"about_ca_topic_score_gemma":0.007743052,"domain_scores_codex":[0.999169,0.0001626725,0.00003660959,0.0002972862,0.0001949,0.0001395837],"domain_scores_gemma":[0.9992774,0.0002376362,0.00005940827,0.0001644903,0.000184839,0.00007621192],"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":[0.0003600758,0.0003554961,0.002063342,0.0001133198,0.0001305575,0.0001104553,0.000132258,0.2650698,0.02473675,0.006236064,0.01348433,0.6872076],"study_design_scores_gemma":[0.00000716559,0.00004843173,0.000248461,0.000006336493,0.00001024898,0.00002372948,0.00001231363,0.9937728,0.00301272,0.002096105,0.000754481,0.000007187614],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01778366,0.0004514796,0.9764073,0.0002992779,0.00009591978,0.00006246693,0.0001490782,0.003500752,0.001250013],"genre_scores_gemma":[0.5157789,0.0006086886,0.4652583,0.001222906,0.000232851,0.0003265837,0.001936967,0.0008760362,0.01375888],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006396461,"threshold_uncertainty_score":0.0127185,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0227818616451936,"score_gpt":0.2630083450867862,"score_spread":0.2402264834415926,"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."}}