{"id":"W1998959036","doi":"10.5244/c.28.46","title":"Unlabelled 3D Motion Examples Improve Cross-View Action Recognition","year":2014,"lang":"en","type":"article","venue":"","topic":"Human Pose and Action Recognition","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Hallucinating; Feature (linguistics); Artificial intelligence; Transformation (genetics); Motion (physics); Viewpoints; Pattern recognition (psychology); Action (physics); Action recognition; Feature learning; Computer vision; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001516518,0.00275969,0.001859164,0.001696246,0.0005369948,0.001880709,0.002628929,0.002285286,0.006037876],"category_scores_gemma":[0.005148723,0.0006116518,0.001618597,0.001231683,0.0006652495,0.002398351,0.002708913,0.002313984,0.006278827],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006360188,"about_ca_system_score_gemma":0.0007628476,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004198496,"about_ca_topic_score_gemma":0.01104722,"domain_scores_codex":[0.9978916,0.0002957182,0.0001060216,0.00102864,0.0004356678,0.0002424662],"domain_scores_gemma":[0.9969923,0.0007811072,0.0001845798,0.00138196,0.0004939988,0.0001660488],"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":[0.0007105006,0.0007960311,0.008230763,0.0003467677,0.000294511,0.0003511184,0.0001632422,0.07230163,0.0417289,0.003004577,0.02657435,0.8454977],"study_design_scores_gemma":[0.00005242393,0.0003125622,0.003893541,0.00006019181,0.00007868849,0.0004085018,0.000136252,0.9546841,0.02598264,0.00513541,0.009215212,0.00004046027],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.140053,0.002072896,0.8125978,0.00040531,0.0007479093,0.0004603916,0.00416236,0.02513933,0.01436101],"genre_scores_gemma":[0.6227813,0.0005183379,0.3467948,0.0005448111,0.0001659132,0.0001802741,0.01839153,0.001230453,0.009392558],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006037876,"threshold_uncertainty_score":0.02019864,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05302742208085452,"score_gpt":0.2913688229776417,"score_spread":0.2383414008967872,"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."}}