{"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":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.000397931,0.0001635629,0.0001467403,0.0003054418,0.001375175,0.0004603218,0.0002439627,0.00007389022,0.00001912513],"category_scores_gemma":[0.000008057366,0.0001757439,0.0001158641,0.000971443,0.00004382796,0.00131257,0.00000246106,0.0001803826,0.00009817705],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004124408,"about_ca_system_score_gemma":0.00004681917,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004838279,"about_ca_topic_score_gemma":0.00000518235,"domain_scores_codex":[0.9986543,0.00003994246,0.0003018371,0.0004115863,0.0002209649,0.0003713549],"domain_scores_gemma":[0.999308,0.00008420503,0.0001319869,0.0002454428,0.0001570745,0.00007324298],"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":[0.00001385622,0.00009707332,0.000001862513,0.000185264,0.00002359439,0.000007884111,0.0005906011,0.02533341,0.01147495,0.000305417,0.001149749,0.9608163],"study_design_scores_gemma":[0.0006176317,0.0001172457,0.00007735188,0.0002967534,0.00004405001,0.00001790876,0.00007266513,0.9355336,0.03816958,0.02413996,0.0005527849,0.0003604157],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005419231,0.00002018974,0.9921051,0.0003442095,0.0004816166,0.0002894772,0.000006659917,0.0009747993,0.0003586624],"genre_scores_gemma":[0.6334217,0.000009889828,0.3650865,0.0002776811,0.0002544642,0.0001995596,0.00002385286,0.00003825158,0.0006881153],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.960456,"threshold_uncertainty_score":0.9999249,"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."}}