{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003214681,0.00005629923,0.00007907888,0.0001957059,0.0001286419,0.00006636998,0.0002148697,0.0000112105,9.743596e-7],"category_scores_gemma":[0.00009548957,0.00005287711,0.00007554693,0.000567892,0.000009716782,0.0009080879,0.00002891731,0.00009370848,0.000009137601],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006176525,"about_ca_system_score_gemma":0.00001999667,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":8.53897e-7,"about_ca_topic_score_gemma":0.000001181486,"domain_scores_codex":[0.9993426,0.00002189022,0.0002414329,0.0001091278,0.0001480701,0.0001368325],"domain_scores_gemma":[0.9992307,0.0001589613,0.000273301,0.000131904,0.0001637808,0.00004134562],"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":[0.00001636897,0.00001963572,0.0005479814,0.00001324912,0.00001174777,0.000008272323,0.0002223536,0.01200777,0.2228069,0.002471312,0.001237602,0.7606368],"study_design_scores_gemma":[0.0004475977,0.00005944234,0.0141552,0.0000324616,0.00001207491,0.0001800189,0.00003220524,0.9133523,0.04069468,0.02483002,0.00608338,0.0001206049],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01359674,0.00006609724,0.9840436,0.001561311,0.0004933397,0.0001097968,4.865817e-7,0.00009087663,0.00003775758],"genre_scores_gemma":[0.9587563,0.00001285022,0.04076139,0.0001070183,0.0003231522,0.000009474416,6.064491e-7,0.000008024273,0.00002118901],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9451596,"threshold_uncertainty_score":0.2156266,"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."}}