{"id":"W3153976895","doi":"10.1109/cvprw53098.2021.00510","title":"Contrastive Learning for Sports Video: Unsupervised Player Classification","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Sports Analytics and Performance","field":"Economics, Econometrics and Finance","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"York University","keywords":"Computer science; Artificial intelligence; Unsupervised learning; Margin (machine learning); Embedding; Machine learning; Frame (networking); A priori and a posteriori; Pattern recognition (psychology)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008679823,0.0009440261,0.0007366974,0.001513379,0.0003432035,0.0009227167,0.001696057,0.0009867972,0.001184965],"category_scores_gemma":[0.003468867,0.0002781092,0.000567469,0.0008568746,0.0006438096,0.001004898,0.001158622,0.001423351,0.0008343029],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000695892,"about_ca_system_score_gemma":0.0003965009,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002967561,"about_ca_topic_score_gemma":0.007133097,"domain_scores_codex":[0.9993686,0.0001601088,0.00001752457,0.0002629597,0.0001092328,0.00008155876],"domain_scores_gemma":[0.9990091,0.0004271645,0.0001490818,0.000202717,0.0001389908,0.00007286293],"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":[0.0007910143,0.0007932099,0.02365921,0.0001791931,0.0002337723,0.0002658284,0.0003047383,0.2301538,0.04000531,0.01181585,0.01184481,0.6799532],"study_design_scores_gemma":[0.00001453461,0.00008371368,0.003865012,0.00001265224,0.00001334164,0.00006921904,0.00004405729,0.9805696,0.00704502,0.006726301,0.001543309,0.00001317644],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1739155,0.0005311196,0.8165731,0.0004512834,0.0001409615,0.0001695799,0.001105091,0.001838421,0.005274822],"genre_scores_gemma":[0.7495966,0.0002620312,0.2412146,0.0002417884,0.0002239487,0.0001537749,0.002682861,0.0002227332,0.005401683],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002967561,"threshold_uncertainty_score":0.005900562,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0639847983599984,"score_gpt":0.2485529312368201,"score_spread":0.1845681328768217,"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."}}