{"id":"W4382775073","doi":"10.3390/jimaging9070132","title":"Motion Vector Extrapolation for Video Object Detection","year":2023,"lang":"en","type":"article","venue":"Journal of Imaging","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Artificial intelligence; Computer vision; Optical flow; Object detection; Motion vector; Extrapolation; Latency (audio); Motion estimation; Motion detection; Low latency (capital markets); Detector; Real-time computing; Motion (physics); Pattern recognition (psychology); Telecommunications","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":[0.0002476878,0.0004926233,0.0003153079,0.0006543217,0.0001776435,0.0003715411,0.0004832476,0.0003276836,0.002169654],"category_scores_gemma":[0.001158144,0.0002258783,0.0002513619,0.0005883278,0.0002012832,0.0007364394,0.0005804407,0.0006383248,0.000837299],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004485261,"about_ca_system_score_gemma":0.0004255834,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00281369,"about_ca_topic_score_gemma":0.003408746,"domain_scores_codex":[0.999849,0.00001830708,0.000006018329,0.00003719212,0.00007346005,0.00001615961],"domain_scores_gemma":[0.9998152,0.00007431972,0.00002634899,0.0000239481,0.00004721909,0.00001302522],"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":[0.0003170032,0.00007230071,0.00130955,0.0001567506,0.00004972402,0.0001808688,0.00008899926,0.05504209,0.1630161,0.009765219,0.005376987,0.7646243],"study_design_scores_gemma":[0.0000118473,0.000131883,0.001790526,0.00003517529,0.00001679913,0.0002503705,0.00003399264,0.9161803,0.06371974,0.008046446,0.009761992,0.00002100022],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03558941,0.002263673,0.9552388,0.0002411223,0.0001048574,0.00007121092,0.0002348028,0.003454868,0.002801292],"genre_scores_gemma":[0.4882516,0.002460147,0.5003399,0.0002572606,0.000131803,0.0001099519,0.0007941863,0.000257783,0.007397294],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00281369,"threshold_uncertainty_score":0.007258177,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02030773970651257,"score_gpt":0.2913831905822255,"score_spread":0.2710754508757129,"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."}}