{"id":"W2114016557","doi":"10.1109/cvpr.2009.5206709","title":"Max-margin hidden conditional random fields for human action recognition","year":2009,"lang":"en","type":"article","venue":"2009 IEEE Conference on Computer Vision and Pattern Recognition","topic":"Human Pose and Action Recognition","field":"Computer Science","cited_by":155,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Conditional random field; Margin (machine learning); Computer science; Action recognition; Artificial intelligence; Pattern recognition (psychology); Action (physics); Machine learning; Physics","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003378723,0.000358544,0.0003478394,0.0003848036,0.000497722,0.000580757,0.0002696262,0.0002569701,0.0003206116],"category_scores_gemma":[0.00001353202,0.0003479161,0.0001698538,0.0001725426,0.00004590702,0.0009902718,0.00003087027,0.0003185135,0.000327666],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004795609,"about_ca_system_score_gemma":0.00003831127,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001263098,"about_ca_topic_score_gemma":0.00002078907,"domain_scores_codex":[0.9978032,0.0001827942,0.0005049797,0.0007794111,0.0003551786,0.0003744927],"domain_scores_gemma":[0.9986639,0.0001949521,0.000271627,0.0002818703,0.0003939238,0.0001937503],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00008823984,0.000238667,0.000009257499,0.00003194149,0.00001987276,0.00001005101,0.000113013,0.000004509637,0.001737533,0.0005660246,0.006269606,0.9909113],"study_design_scores_gemma":[0.01915859,0.01064675,0.01587537,0.00184069,0.0001615664,0.0003382751,0.00009908854,0.2641699,0.03000744,0.6495503,0.005241451,0.002910609],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1695454,0.00001541899,0.8257599,0.00173956,0.0009683088,0.0006839285,0.0001208748,0.0002797871,0.0008868421],"genre_scores_gemma":[0.9870051,0.0001103158,0.006417305,0.004220096,0.0007949651,0.00008595344,0.001179345,0.00001714561,0.0001698233],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9880007,"threshold_uncertainty_score":0.9998973,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09186341716089508,"score_gpt":0.325554323323797,"score_spread":0.2336909061629019,"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."}}