{"id":"W4250766080","doi":"10.32920/ryerson.14648964.v1","title":"Multimodal Information Fusion for Human Action Recognition","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Human Pose and Action Recognition","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Artificial intelligence; Computer science; Discriminative model; Pattern recognition (psychology); Representation (politics); Pooling; Locality; Modalities; RGB color model; Canonical correlation; Deep learning; Feature learning; Machine learning","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":[],"consensus_categories":[],"category_scores_codex":[0.0002576609,0.0002203883,0.0002085606,0.0003024676,0.0003172049,0.0009257194,0.0003263948,0.0003565288,0.0002463217],"category_scores_gemma":[0.00004580276,0.0002328728,0.0002159329,0.0001275904,0.00001308445,0.002272267,0.0004094715,0.0003478667,0.0001484892],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001226439,"about_ca_system_score_gemma":0.0001090581,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001248128,"about_ca_topic_score_gemma":0.00008484106,"domain_scores_codex":[0.9986029,0.00006190669,0.0004559829,0.0004051768,0.0002657542,0.0002082925],"domain_scores_gemma":[0.9985338,0.0000467802,0.0003377275,0.0004236559,0.0005838842,0.00007408037],"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":[0.00001455338,0.0001463375,0.000009869577,0.0004219448,0.0000493726,0.000001547769,0.0008989135,0.00013497,0.003764215,0.001866151,0.003156029,0.9895361],"study_design_scores_gemma":[0.005335283,0.0006480548,0.004527005,0.001449897,0.0002405752,0.00007930728,0.001692366,0.4720828,0.2577233,0.2113694,0.0412118,0.00364022],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1139509,0.000008862386,0.8786088,0.0003168333,0.001770735,0.0007217304,0.00002903583,0.0004681896,0.004124948],"genre_scores_gemma":[0.8683485,0.00008383286,0.1213439,0.001059818,0.000651982,0.0005959781,0.007374364,0.00002119635,0.0005203385],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9858959,"threshold_uncertainty_score":0.9496277,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07079110847569632,"score_gpt":0.3124610884846966,"score_spread":0.2416699800090003,"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."}}