{"id":"W4312924718","doi":"10.1109/icpr56361.2022.9955633","title":"Learning Sequential Contexts using Transformer for 3D Hand Pose Estimation","year":2022,"lang":"en","type":"article","venue":"2022 26th International Conference on Pattern Recognition (ICPR)","topic":"Human Pose and Action Recognition","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Computer science; Transformer; Artificial intelligence; Encoder; Convolutional neural network; Pose; Pipeline (software); Pattern recognition (psychology); Graph; Deep learning; Computer vision; Machine learning; Theoretical computer science; 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":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004496053,0.000241923,0.0002076985,0.0004305775,0.000919695,0.0004990126,0.0005153648,0.00007007742,0.006588272],"category_scores_gemma":[0.00004961065,0.0002798083,0.0001690408,0.0001943922,0.00004866113,0.0008556057,0.00009992202,0.0004596866,0.0001682137],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002628882,"about_ca_system_score_gemma":0.0001471235,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005162881,"about_ca_topic_score_gemma":0.00002087312,"domain_scores_codex":[0.9976867,0.0002070544,0.000483269,0.0005948386,0.0007153276,0.0003128507],"domain_scores_gemma":[0.9988602,0.0001037622,0.0003248716,0.0001747845,0.0004378589,0.00009852305],"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":[0.0001696222,0.0003092908,0.0001236018,0.00003511597,0.0001282953,0.00002317173,0.001144082,0.002410963,0.01163984,0.002806633,0.0006479167,0.9805615],"study_design_scores_gemma":[0.002282038,0.0007585813,0.0002377463,0.0001069918,0.00004377759,0.0001127731,0.0003724305,0.9671294,0.01013181,0.01185727,0.006392926,0.0005742708],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1103426,0.00001059459,0.8810574,0.001182782,0.002501955,0.0005891853,0.0003968454,0.0001684324,0.003750136],"genre_scores_gemma":[0.9914197,0.00001761319,0.00438212,0.001374563,0.0002720086,0.0003700444,0.001355037,0.00002830196,0.0007806167],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9799872,"threshold_uncertainty_score":0.9999654,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09094453092168146,"score_gpt":0.3216653871206054,"score_spread":0.230720856198924,"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."}}