{"id":"W2049819452","doi":"10.1109/iccv.2007.4409073","title":"Human Pose Estimation using Motion Exemplars","year":2007,"lang":"en","type":"article","venue":"","topic":"Human Pose and Action Recognition","field":"Computer Science","cited_by":42,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Artificial intelligence; Computer science; Computer vision; Motion (physics); Similarity (geometry); Position (finance); Inference; Joint (building); Motion estimation; Sampling (signal processing); Pose; Motion capture; Measure (data warehouse); Pattern recognition (psychology); Image (mathematics); Data mining; Engineering","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027281,0.00005609798,0.00004610945,0.0001335535,0.0002264674,0.00009772345,0.0001187146,0.00003654798,0.0001006636],"category_scores_gemma":[0.000006493707,0.00005518995,0.00002672329,0.0001625342,0.000009588912,0.0007388297,0.00003019681,0.00005056163,0.00012057],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005408301,"about_ca_system_score_gemma":0.000007322648,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003080202,"about_ca_topic_score_gemma":0.0000162973,"domain_scores_codex":[0.9994259,0.00001356716,0.000144825,0.000147953,0.0001335538,0.0001341961],"domain_scores_gemma":[0.9997025,0.00001109375,0.00005049026,0.0001430471,0.00004769438,0.00004525061],"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":[0.000004029995,0.0002383332,0.0004988068,0.00002217131,0.00001778473,0.00003510824,0.0008137415,0.001559462,0.1063393,0.1480373,0.001363767,0.7410702],"study_design_scores_gemma":[0.0008312718,0.0001329631,0.02937335,0.00005062533,0.00001883592,0.0001465161,0.00009833927,0.71835,0.1814466,0.06809305,0.0008882039,0.0005701997],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.318544,0.000001817823,0.6763112,0.00005925613,0.0001475387,0.00004404727,1.181258e-7,0.0001358876,0.004756094],"genre_scores_gemma":[0.9257937,2.605981e-7,0.07365493,0.0002053703,0.00006741769,6.225454e-7,0.000004254035,0.000003404402,0.0002700568],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7405,"threshold_uncertainty_score":0.2250581,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04043340967132915,"score_gpt":0.3155299123910811,"score_spread":0.275096502719752,"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."}}