{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001089723,0.0009825259,0.0006429817,0.0007453553,0.0002575399,0.0009145918,0.002293912,0.0006860815,0.004991886],"category_scores_gemma":[0.003832795,0.0004846698,0.0006743736,0.0007122664,0.0004555382,0.00245092,0.001449582,0.001497546,0.002389974],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007985272,"about_ca_system_score_gemma":0.001062441,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007963204,"about_ca_topic_score_gemma":0.007189297,"domain_scores_codex":[0.9993923,0.00008997192,0.00003424172,0.0001804874,0.0002364962,0.00006658409],"domain_scores_gemma":[0.9991893,0.0003126824,0.00007026835,0.0001674542,0.000210142,0.00005014014],"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":[0.0006439786,0.0001714669,0.001609968,0.0001682506,0.00008366239,0.000199086,0.0001221548,0.1664112,0.03149589,0.03399266,0.01255814,0.7525436],"study_design_scores_gemma":[0.00001035947,0.00004017304,0.0001508931,0.000006652049,0.00001192439,0.00005739351,0.000006993635,0.9860337,0.006139987,0.005614019,0.001918762,0.000009132316],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003855324,0.0002167561,0.9919344,0.00006209414,0.00004850316,0.00004423716,0.0001269705,0.002852019,0.0008596707],"genre_scores_gemma":[0.4920903,0.0009329172,0.4970468,0.0002740645,0.0001681145,0.0002135566,0.001542827,0.000764069,0.006967311],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007963204,"threshold_uncertainty_score":0.01669955,"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."}}