{"id":"W2159993056","doi":"10.5244/c.25.132","title":"Latent Boosting for Action Recognition","year":2011,"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":"Simon Fraser University","funders":"","keywords":"Boosting (machine learning); Latent variable; Computer science; Machine learning; Artificial intelligence; Action recognition; Gradient boosting; Latent variable model; Margin (machine learning); Pattern recognition (psychology)","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":[0.002632737,0.001038335,0.001879244,0.0009523717,0.0004718317,0.001230508,0.001918275,0.001049702,0.004915821],"category_scores_gemma":[0.004557745,0.0005439293,0.001222941,0.001024586,0.0008681725,0.001817249,0.00120071,0.002260326,0.003149383],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001012388,"about_ca_system_score_gemma":0.0007791102,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002328609,"about_ca_topic_score_gemma":0.002904087,"domain_scores_codex":[0.9985445,0.0006167328,0.00005330752,0.0003624787,0.0002740154,0.0001490619],"domain_scores_gemma":[0.998193,0.0007856892,0.0001595693,0.0004754415,0.0002793215,0.0001070465],"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":[0.0003164225,0.0003604672,0.002553866,0.000311216,0.0002345864,0.00009254157,0.0001535519,0.256564,0.01014846,0.06960168,0.02037957,0.6392836],"study_design_scores_gemma":[0.00001048534,0.00004094291,0.000311579,0.00001422473,0.00001618244,0.00003048988,0.000006692465,0.9642617,0.00154981,0.03034291,0.0034053,0.000009668605],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00436164,0.000880792,0.9916082,0.0001764908,0.00008998263,0.00003798082,0.000123249,0.00127936,0.001442295],"genre_scores_gemma":[0.4099004,0.001013422,0.5769178,0.0005736552,0.0004113165,0.0002598965,0.001776175,0.0005086954,0.008638643],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004915821,"threshold_uncertainty_score":0.0164451,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2685263825915866,"score_gpt":0.2921282165624963,"score_spread":0.02360183397090976,"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."}}