{"id":"W2492904644","doi":"10.5244/c.30.26","title":"Play and Learn: Using Video Games to Train Computer Vision Models","year":2016,"lang":"en","type":"preprint","venue":"","topic":"Human Pose and Action Recognition","field":"Computer Science","cited_by":40,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; RGB color model; Convolutional neural network; Artificial intelligence; Synthetic data; Segmentation; Video game; Computer vision; Adaptation (eye); Artificial neural network; Image (mathematics); Machine learning; Multimedia","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":[0.0002156949,0.0002101152,0.0002318902,0.0002299706,0.0001074361,0.0004709035,0.0003798314,0.0001662555,0.00006586308],"category_scores_gemma":[0.00000424958,0.0001614182,0.00007351925,0.00006823915,0.00002489341,0.0005293742,0.001117409,0.0002052974,0.00008120594],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004500305,"about_ca_system_score_gemma":0.00005233935,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002582728,"about_ca_topic_score_gemma":0.000006736897,"domain_scores_codex":[0.9985618,0.00007207781,0.0002403101,0.000684533,0.0002231413,0.0002181331],"domain_scores_gemma":[0.99922,0.0000608321,0.0000870404,0.0004012584,0.00008152992,0.0001493099],"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":[0.00001705902,0.00008775626,0.000009683506,0.00009746259,0.0000602672,0.00002861544,0.002170414,0.01511742,0.003365548,0.02057083,0.01697636,0.9414986],"study_design_scores_gemma":[0.0002690443,0.00009608261,0.0001341106,0.0004319624,0.000008975417,0.00003494196,0.00000887129,0.9283652,0.001291324,0.06525947,0.003665484,0.0004345065],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04270189,0.00002896395,0.9517024,0.00125723,0.0005498691,0.0002462302,0.000006353051,0.0001954065,0.003311645],"genre_scores_gemma":[0.7494964,0.00003900012,0.2461894,0.00225747,0.0006459645,0.00001834325,0.000007282677,0.00002498734,0.001321153],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9410641,"threshold_uncertainty_score":0.6582443,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04886486217382408,"score_gpt":0.2963733462129353,"score_spread":0.2475084840391112,"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."}}