{"id":"W3155172600","doi":"10.1109/cvprw53098.2021.00511","title":"Camera Calibration and Player Localization in SoccerNet-v2 and Investigation of their Representations for Action Spotting","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Human Pose and Action Recognition","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"Fonds pour la Formation à la Recherche dans l’Industrie et dans l’Agriculture; King Abdullah University of Science and Technology; Waalse Gewest","keywords":"Spotting; Leverage (statistics); Computer science; Exploit; Artificial intelligence; Calibration; Focus (optics); Artificial neural network; Task (project management); Scale (ratio); Machine learning; Computer vision; Computer security; Engineering; Mathematics; Cartography; Geography","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008675862,0.002893674,0.0009419695,0.001093295,0.000484349,0.001194228,0.002393155,0.00179108,0.006249652],"category_scores_gemma":[0.002765076,0.0005343207,0.0007972893,0.001019172,0.0006273967,0.001594388,0.001405391,0.002508076,0.003159687],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001394949,"about_ca_system_score_gemma":0.001244974,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01910131,"about_ca_topic_score_gemma":0.03106183,"domain_scores_codex":[0.9993269,0.00009430348,0.00001711888,0.0002935795,0.0001052463,0.000162834],"domain_scores_gemma":[0.9996463,0.00008065735,0.00003597521,0.000098674,0.00008631669,0.00005212549],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001145585,0.0007225803,0.00789824,0.000504281,0.0003122104,0.0003754194,0.0002478685,0.2743101,0.02015328,0.003600344,0.06690686,0.6238232],"study_design_scores_gemma":[0.00006385455,0.0002395175,0.003753717,0.00008599382,0.00005154511,0.0001565103,0.0001390906,0.9714797,0.01288643,0.003013074,0.008093436,0.00003715992],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6400203,0.006991267,0.2613795,0.002001883,0.001963512,0.0005815771,0.01191451,0.04247861,0.03266891],"genre_scores_gemma":[0.8802204,0.0009755056,0.07085126,0.0007355006,0.000163074,0.0003057116,0.02954488,0.001053828,0.01614988],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01910131,"threshold_uncertainty_score":0.03798026,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06240400337999324,"score_gpt":0.3001595081641901,"score_spread":0.2377555047841968,"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."}}