{"id":"W4392903121","doi":"10.1109/icassp48485.2024.10446305","title":"Ranking of Visual Trackers Using Robust Error Norms","year":2024,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"BitTorrent tracker; Outlier; Computer science; Enhanced Data Rates for GSM Evolution; Artificial intelligence; Estimator; Ranking (information retrieval); Computer vision; Video tracking; Tracking (education); Measure (data warehouse); Mathematics; Statistics; Eye tracking; Object (grammar); Data mining","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":[0.000800155,0.00009949487,0.0001632256,0.000143007,0.00005044147,0.0001449035,0.0003375542,0.00004621725,0.00003194954],"category_scores_gemma":[0.00003015658,0.00007761623,0.00009872719,0.0005974761,0.00003477901,0.0004937999,0.00008846309,0.0001076755,0.00001736745],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002291517,"about_ca_system_score_gemma":0.00008061005,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007006308,"about_ca_topic_score_gemma":0.00001545248,"domain_scores_codex":[0.9989913,0.00006733651,0.0002247395,0.0002851152,0.0002237628,0.0002077894],"domain_scores_gemma":[0.9994788,0.0001696171,0.00003552297,0.0002314784,0.00004343707,0.0000411615],"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":[0.00002186592,0.0001940756,0.01072174,0.000542632,0.0002458505,0.0002535707,0.004059891,0.03035487,0.04632818,0.09105642,0.0006020622,0.8156188],"study_design_scores_gemma":[0.0001554559,0.00005470015,0.002552578,0.0001172745,0.000009558715,0.00004294384,0.00005417545,0.9796017,0.01452487,0.001995004,0.0006947413,0.0001969733],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1314901,0.0002679041,0.8653592,0.0001601041,0.0006907759,0.00005030973,5.348679e-7,0.0002189334,0.00176213],"genre_scores_gemma":[0.7365914,0.000004378765,0.2631847,0.00005935916,0.00005611482,9.001248e-7,3.904607e-7,0.000007929116,0.00009488513],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9492468,"threshold_uncertainty_score":0.3165098,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07036390172944297,"score_gpt":0.3578511518061807,"score_spread":0.2874872500767378,"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."}}