{"id":"W2897585580","doi":"10.1109/tmm.2018.2875510","title":"Multimodal Learning for Human Action Recognition Via Bimodal/Multimodal Hybrid Centroid Canonical Correlation Analysis","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Human Pose and Action Recognition","field":"Computer Science","cited_by":49,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Canonical correlation; Centroid; Computer science; Modalities; Artificial intelligence; Pattern recognition (psychology); Discriminative model; Correlation; Feature vector; Feature (linguistics); Machine learning; Mathematics","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":[0.0009382889,0.001237703,0.001277774,0.001366802,0.0003852342,0.0007903968,0.001190437,0.0006757224,0.003066512],"category_scores_gemma":[0.002686251,0.0003165314,0.001060215,0.001975147,0.0008879771,0.0009798715,0.001618123,0.001235985,0.001451525],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005557906,"about_ca_system_score_gemma":0.001124144,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006285624,"about_ca_topic_score_gemma":0.008481325,"domain_scores_codex":[0.9988422,0.0002664554,0.00003643332,0.0004275801,0.000288021,0.0001394008],"domain_scores_gemma":[0.9992839,0.0002123338,0.0000753577,0.0001297751,0.0002292893,0.0000693345],"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":[0.0003228271,0.0002210466,0.004706307,0.0001721078,0.0001930021,0.0002029781,0.0001922267,0.1276182,0.02296972,0.01108834,0.008918444,0.8233948],"study_design_scores_gemma":[0.000007437157,0.00007231838,0.002089052,0.0000167199,0.00002929669,0.0001273908,0.00005511969,0.9837045,0.004387823,0.007394403,0.002089182,0.00002675004],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01400915,0.0005978175,0.9827574,0.0001539414,0.00005484116,0.0000469735,0.0001539413,0.0007600684,0.001465836],"genre_scores_gemma":[0.6260266,0.001249772,0.3636486,0.0004115627,0.0003195064,0.0002979661,0.001530843,0.0003065857,0.006208716],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006285624,"threshold_uncertainty_score":0.01249808,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03197822218404654,"score_gpt":0.2916044915662906,"score_spread":0.259626269382244,"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."}}