{"id":"W4394597906","doi":"10.1109/wacv57701.2024.00677","title":"MotionAGFormer: Enhancing 3D Human Pose Estimation with a Transformer-GCNFormer Network","year":2024,"lang":"en","type":"article","venue":"","topic":"Non-Invasive Vital Sign Monitoring","field":"Engineering","cited_by":135,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Pose; Computer science; Transformer; 3D pose estimation; Artificial intelligence; Computer vision; Engineering; Electrical engineering; Voltage","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":[0.0006292858,0.001648989,0.0008299514,0.0008947073,0.0002634133,0.0005196859,0.001676989,0.0009702473,0.004808828],"category_scores_gemma":[0.001911817,0.0006477556,0.000883902,0.001006635,0.0005347344,0.001011764,0.001309005,0.001051502,0.002225379],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000520926,"about_ca_system_score_gemma":0.0007290408,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01046935,"about_ca_topic_score_gemma":0.02042886,"domain_scores_codex":[0.9997159,0.00005184409,0.00000790309,0.00009938562,0.00008300162,0.00004205888],"domain_scores_gemma":[0.9997268,0.0001005092,0.00002244053,0.0000485072,0.00007877956,0.00002298483],"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":[0.0003783845,0.0001263892,0.001103841,0.0001368471,0.0001549389,0.0001874573,0.00009998894,0.1495271,0.0365849,0.003498356,0.01804357,0.7901582],"study_design_scores_gemma":[0.00002453368,0.00009187817,0.0007051201,0.00001639363,0.00004856025,0.0002164626,0.00001487368,0.9792954,0.01080995,0.004130155,0.004625142,0.0000215919],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007361565,0.0005820398,0.9855261,0.0001781804,0.000151417,0.00005822266,0.0003604729,0.004065661,0.00171626],"genre_scores_gemma":[0.3646187,0.001744051,0.6075235,0.001181482,0.0003416156,0.0003102516,0.003291768,0.001128856,0.01985984],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01046935,"threshold_uncertainty_score":0.0208168,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006069140902861225,"score_gpt":0.2150099757425329,"score_spread":0.2089408348396717,"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."}}