{"id":"W4383890459","doi":"10.1109/tip.2023.3275914","title":"Regular Splitting Graph Network for 3D Human Pose Estimation","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Human Pose and Action Recognition","field":"Computer Science","cited_by":52,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Adjacency matrix; Pose; Computer science; Graph; Artificial intelligence; Pattern recognition (psychology); Theoretical computer science; Graph theory; Algorithm; Mathematics; Combinatorics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.000366357,0.001195434,0.0006143311,0.0009850467,0.0002484036,0.0004372473,0.001098344,0.000853863,0.002252155],"category_scores_gemma":[0.0014257,0.0005295405,0.0006819268,0.000980543,0.0005326315,0.001086287,0.0007346471,0.0009155422,0.001084985],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006726095,"about_ca_system_score_gemma":0.0005570764,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01063973,"about_ca_topic_score_gemma":0.01487422,"domain_scores_codex":[0.999741,0.00005308459,0.00000756826,0.0001014158,0.00006915327,0.00002774198],"domain_scores_gemma":[0.9997138,0.0001188157,0.0000385947,0.00004331566,0.00006677927,0.00001865465],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001817974,0.00005014137,0.001163412,0.00007587535,0.00007502292,0.0001151301,0.0000629874,0.7251916,0.00921259,0.00618851,0.003700832,0.2539821],"study_design_scores_gemma":[0.000002950075,0.0000137662,0.0002510676,0.000004744887,0.000005925631,0.00002820734,0.000005235638,0.9934423,0.0009950855,0.004613128,0.0006319287,0.00000557182],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01551728,0.0004053189,0.980725,0.0001372825,0.00004351056,0.00003243372,0.0002860542,0.001623083,0.001229895],"genre_scores_gemma":[0.6841739,0.001098368,0.3031473,0.0003708448,0.0001157816,0.0001849442,0.00261729,0.0003929661,0.007898623],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01063973,"threshold_uncertainty_score":0.0211556,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02536447945263329,"score_gpt":0.2923261692094831,"score_spread":0.2669616897568498,"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."}}