{"id":"W3172128134","doi":"10.23919/mva51890.2021.9511378","title":"Predicting Next Local Appearance for Video Anomaly Detection","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Institut de Valorisation des Données","keywords":"Artificial intelligence; Anomaly detection; Computer science; Object (grammar); Inference; Computer vision; Frame (networking); Anomaly (physics); Pattern recognition (psychology); Adversarial system; Image (mathematics)","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":[0.0007846831,0.0009196571,0.0009618647,0.001261367,0.0003464331,0.0005532014,0.002018651,0.0009117984,0.00155594],"category_scores_gemma":[0.004096561,0.0003616418,0.0006254197,0.0008518298,0.0006380228,0.001209951,0.001082549,0.001813127,0.0008342261],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006440685,"about_ca_system_score_gemma":0.0005195849,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00395592,"about_ca_topic_score_gemma":0.004730625,"domain_scores_codex":[0.9993462,0.00009466185,0.00002170102,0.0002144714,0.0002431079,0.00007984139],"domain_scores_gemma":[0.9983901,0.0005511869,0.0002299769,0.0003410391,0.0003701955,0.0001176183],"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":[0.0003389662,0.0002951676,0.01113813,0.0001228816,0.0001441823,0.0004304734,0.0001215621,0.3294599,0.04696872,0.00886278,0.008375007,0.5937422],"study_design_scores_gemma":[0.000003013366,0.00002286802,0.0004480498,0.000002974044,0.0000068944,0.00008581288,0.000005166347,0.9917951,0.004140019,0.002850072,0.0006340272,0.000006055893],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01852093,0.0001666926,0.9789651,0.00009471168,0.0000462365,0.00003203534,0.00008734473,0.001595676,0.0004912324],"genre_scores_gemma":[0.5607498,0.000357039,0.4343674,0.0001673579,0.0001671677,0.00007776216,0.0006885073,0.0003555188,0.003069433],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00395592,"threshold_uncertainty_score":0.007865787,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0215842188311677,"score_gpt":0.2611664943828155,"score_spread":0.2395822755516478,"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."}}