{"id":"W4206004830","doi":"10.1109/tmm.2022.3141888","title":"Fast Human Pose Estimation in Compressed Videos","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Human Pose and Action Recognition","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba; Huawei Technologies (Canada); McMaster University","funders":"","keywords":"Computer science; Image warping; Artificial intelligence; Computer vision; Pose; Dynamic time warping; Frame (networking); Motion estimation; Inter frame; Discrete cosine transform; Motion (physics); Pattern recognition (psychology); Reference frame; Image (mathematics)","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":[0.0001706359,0.0001326105,0.0001340737,0.0004295046,0.0005067369,0.00007130291,0.0003631651,0.00004068434,0.0007505529],"category_scores_gemma":[0.000002616964,0.0001542286,0.00007577339,0.0004501673,0.00002847484,0.0004527613,0.000004830396,0.0004175525,0.0001907442],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001367518,"about_ca_system_score_gemma":0.00003563114,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001011285,"about_ca_topic_score_gemma":0.00007789298,"domain_scores_codex":[0.9987023,0.0001443232,0.0002679821,0.000332443,0.0003396444,0.0002133166],"domain_scores_gemma":[0.9994098,0.0001034661,0.00007263542,0.0003061979,0.0000322652,0.00007561157],"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":[0.00004747725,0.001735406,0.00003080679,0.00002073789,0.00003766817,0.00006217167,0.004020078,0.4600174,0.0265135,0.0002824687,0.00101007,0.5062222],"study_design_scores_gemma":[0.001419901,0.0001980108,0.001037562,0.00001825709,0.00001012015,0.00001970008,0.0001250459,0.9683235,0.02726785,0.0006516356,0.0006587717,0.0002696288],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05532707,0.000006456302,0.9420974,0.0003068773,0.001005319,0.0003025976,0.00002897251,0.0002582862,0.0006669936],"genre_scores_gemma":[0.9903917,0.000002705548,0.008683182,0.0002988067,0.00002665368,0.000207612,0.0000176651,0.00001263444,0.000359091],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9350646,"threshold_uncertainty_score":0.8218027,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02036827576848993,"score_gpt":0.2654056436825718,"score_spread":0.2450373679140819,"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."}}