{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001488823,0.002387274,0.0005295234,0.0009519432,0.0003486772,0.001071693,0.001990118,0.001297458,0.00171418],"category_scores_gemma":[0.005393912,0.0007749135,0.0008451089,0.0005831834,0.0006774431,0.001650001,0.00108309,0.002094725,0.001007289],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001430629,"about_ca_system_score_gemma":0.0006775948,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0214036,"about_ca_topic_score_gemma":0.03220299,"domain_scores_codex":[0.9993218,0.0002300796,0.00002736609,0.0002436021,0.00008541604,0.00009167772],"domain_scores_gemma":[0.9988174,0.0005690875,0.00007653874,0.0002575781,0.0001955022,0.00008388422],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0012886,0.001644635,0.02017353,0.0003407025,0.0005384423,0.0003009093,0.0003460573,0.5421185,0.01470518,0.002750592,0.01791724,0.3978756],"study_design_scores_gemma":[0.00005013329,0.0002374648,0.002037881,0.00001926434,0.00002446253,0.00003750444,0.00006052456,0.9873946,0.006997883,0.001577443,0.001548145,0.00001467838],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7277152,0.001550936,0.2476211,0.0008434677,0.0005449934,0.0006667958,0.003141326,0.01112175,0.006794481],"genre_scores_gemma":[0.8542829,0.0002891923,0.1333217,0.0004154533,0.00005744776,0.0002802504,0.007654207,0.0002999709,0.003398825],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0214036,"threshold_uncertainty_score":0.04255807,"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."}}