{"id":"W4392033553","doi":"10.32920/25266754.v1","title":"Motion Vector Extrapolation for Video Object Detection","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Image Processing Techniques and Applications","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Extrapolation; Computer vision; Motion vector; Artificial intelligence; Computer science; Motion (physics); Object (grammar); Object detection; Computer graphics (images); Pattern recognition (psychology); Mathematics; Statistics; Image (mathematics)","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":[],"consensus_categories":[],"category_scores_codex":[0.0001042865,0.0001675296,0.0001190884,0.0001287461,0.00005413792,0.0001490872,0.0000945439,0.0001911891,0.00002008538],"category_scores_gemma":[0.00001246428,0.0001726019,0.0001075671,0.0001060624,0.000009318285,0.00004656165,0.00007107664,0.00031651,0.00002755419],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001326494,"about_ca_system_score_gemma":0.00001830689,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000019997,"about_ca_topic_score_gemma":0.00001755803,"domain_scores_codex":[0.9993512,0.000004230835,0.0001949582,0.0002505578,0.00007005267,0.0001289685],"domain_scores_gemma":[0.9996453,0.00001999778,0.00002940932,0.0002206385,0.0000599692,0.00002470884],"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":[0.00001586792,0.00004581614,0.000003881104,0.006649346,0.0001463132,9.626378e-7,0.0002846728,0.02425532,0.1864409,0.007341105,0.01260212,0.7622136],"study_design_scores_gemma":[0.00004104148,0.00001014257,0.0000397082,0.0001062638,0.00005692161,0.000002569426,0.000005272404,0.7469493,0.1639589,0.08238193,0.006221161,0.0002267547],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002120215,0.0005264286,0.9885649,0.00009668902,0.0006150116,0.0006805446,0.00003635796,0.003547058,0.003812771],"genre_scores_gemma":[0.951676,0.00003085543,0.04626692,0.00001633388,0.0003386706,0.00122653,0.00008012047,0.00007234953,0.0002922545],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9495558,"threshold_uncertainty_score":0.7038503,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01685534127301436,"score_gpt":0.2696936239322087,"score_spread":0.2528382826591944,"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."}}