{"id":"W4312305807","doi":"10.1109/icpr56361.2022.9956707","title":"VPTR: Efficient Transformers for Video Prediction","year":2022,"lang":"en","type":"article","venue":"2022 26th International Conference on Pattern Recognition (ICPR)","topic":"Image and Video Quality Assessment","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Computer science; Autoregressive model; Inference; Transformer; Ground truth; Artificial intelligence; Source code; Machine learning; Pattern recognition (psychology); Data mining; Engineering","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0007122013,0.0002157136,0.0001844973,0.000320992,0.0004355467,0.0002577273,0.0007850884,0.0000434699,0.003322605],"category_scores_gemma":[0.00003851463,0.0002361017,0.0001891017,0.0002260435,0.00003486492,0.0003676315,0.000192525,0.0003538571,0.0001392833],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003345125,"about_ca_system_score_gemma":0.0001686705,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000437523,"about_ca_topic_score_gemma":0.00001241117,"domain_scores_codex":[0.9973256,0.0001860174,0.0004962679,0.0006538791,0.001009373,0.0003288815],"domain_scores_gemma":[0.9989595,0.0001291339,0.0002232336,0.0002372225,0.0003446296,0.0001062579],"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":[0.0002928909,0.00135004,0.000488304,0.00008067469,0.0002477886,0.00004190154,0.001883162,0.001198556,0.003275286,0.02695618,0.01289803,0.9512872],"study_design_scores_gemma":[0.003639912,0.001672097,0.001793183,0.0001156795,0.00004758659,0.00008318538,0.00175673,0.9242233,0.007565494,0.01398906,0.04426216,0.0008515986],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01968455,0.00001374745,0.956895,0.007890876,0.003390986,0.0007607665,0.001487041,0.0002142726,0.009662772],"genre_scores_gemma":[0.9908448,0.00001875077,0.001208394,0.005080975,0.0001956911,0.00113922,0.0008480472,0.00002042832,0.000643735],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9711602,"threshold_uncertainty_score":0.9975885,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08701592930836817,"score_gpt":0.3221272033111226,"score_spread":0.2351112740027544,"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."}}