{"id":"W3129915554","doi":"10.1109/tpami.2022.3151865","title":"Deep Gait Recognition: A Survey","year":2022,"lang":"en","type":"preprint","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Gait Recognition and Analysis","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Mitacs","keywords":"Biometrics; Gait; Deep learning; Computer science; Artificial intelligence; Discriminative model; Representation (politics); Field (mathematics); Taxonomy (biology); Feature learning; Modality (human–computer interaction); Machine learning; Data science; Physical medicine and rehabilitation","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004213782,0.0005216692,0.0007841582,0.001302814,0.0002391799,0.0001811269,0.0003362797,0.0002080087,0.01284613],"category_scores_gemma":[0.000006746131,0.000549075,0.0008019417,0.001336563,0.00006053178,0.00007125828,0.0000138904,0.001342298,0.0001393819],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001075582,"about_ca_system_score_gemma":0.00001925526,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002821304,"about_ca_topic_score_gemma":0.01338518,"domain_scores_codex":[0.997622,0.0002416648,0.0006908044,0.0007277295,0.0003975608,0.0003203061],"domain_scores_gemma":[0.9987813,0.0002008207,0.0001258367,0.0005675033,0.000117523,0.0002070173],"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":[0.00000995021,0.0001096653,0.0005610934,0.00008522643,0.002792567,0.00001283834,0.0001365989,0.4782753,0.00001095971,2.492497e-7,0.00002775621,0.5179778],"study_design_scores_gemma":[0.0001581071,0.00008239243,0.004023331,0.00007702249,0.005633554,0.00001258191,0.000210441,0.9783214,0.009486143,0.000358462,0.0002920265,0.001344529],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008768884,0.0004546319,0.9875785,0.00005561056,0.0005731019,0.0001838281,0.001587116,0.0002857324,0.0005126327],"genre_scores_gemma":[0.9936176,0.004336282,0.0002481217,0.0002361462,0.00004124724,0.0001762501,0.000985931,0.00006083231,0.0002975861],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9873303,"threshold_uncertainty_score":0.9996961,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03772986746056348,"score_gpt":0.2664334556946217,"score_spread":0.2287035882340583,"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."}}