{"id":"W2782082266","doi":"10.1109/ism.2017.30","title":"Heterogeneous Features Fusion with Collaborative Representation Learning for 3D Action Recognition","year":2017,"lang":"en","type":"article","venue":"","topic":"Human Pose and Action Recognition","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Representation (politics); Discriminative model; Computer science; Artificial intelligence; Action recognition; Feature (linguistics); Feature learning; Sequence (biology); Task (project management); Pattern recognition (psychology); Action (physics); Feature extraction; Feature vector; Machine learning; Class (philosophy)","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":[0.0007615424,0.001309803,0.001475784,0.001093923,0.0002927688,0.0006711432,0.001440554,0.0008526605,0.001688992],"category_scores_gemma":[0.001697458,0.0003826675,0.001401,0.001717638,0.0005476975,0.001382661,0.001693878,0.00118955,0.0007665411],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005267896,"about_ca_system_score_gemma":0.000704419,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004247246,"about_ca_topic_score_gemma":0.004118682,"domain_scores_codex":[0.9991949,0.0001314976,0.00004060813,0.000291082,0.0002231422,0.0001187737],"domain_scores_gemma":[0.9994783,0.0001297344,0.00008086058,0.0001587582,0.0001076162,0.00004471102],"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":[0.0002277321,0.0002418628,0.001703095,0.0001050688,0.0001769562,0.0001655002,0.0001096204,0.1643593,0.0292026,0.004813511,0.005799911,0.7930949],"study_design_scores_gemma":[0.000009057923,0.00008705004,0.0008581679,0.000009331765,0.00003878961,0.00007398015,0.00002381548,0.9846854,0.008334836,0.004590515,0.00127017,0.0000188239],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01434144,0.0004536965,0.9832208,0.0000796452,0.00004196207,0.00003120623,0.0001367784,0.001075661,0.0006188083],"genre_scores_gemma":[0.6866452,0.0006375645,0.3079122,0.0002769066,0.0001251973,0.0001504512,0.001470975,0.0001732238,0.002608307],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004247246,"threshold_uncertainty_score":0.008445024,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05281045456844188,"score_gpt":0.3239450548318696,"score_spread":0.2711346002634277,"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."}}