{"id":"W4392033643","doi":"10.32920/25266754","title":"Motion Vector Extrapolation for Video Object Detection","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Artificial intelligence; Extrapolation; Computer vision; Optical flow; Object detection; Motion vector; Latency (audio); Motion estimation; Detector; Low latency (capital markets); Motion detection; Real-time computing; Motion (physics); Pattern recognition (psychology); Telecommunications; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002267863,0.0004999828,0.0003337268,0.0006118322,0.0001558233,0.0004584852,0.0005043605,0.0003927445,0.003194922],"category_scores_gemma":[0.001220302,0.0002531395,0.000241972,0.0005770463,0.0002562489,0.0008507732,0.0006059166,0.0007616103,0.001222575],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004796096,"about_ca_system_score_gemma":0.0003320872,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003101269,"about_ca_topic_score_gemma":0.002649168,"domain_scores_codex":[0.9998399,0.00002157896,0.000006176878,0.00004307818,0.00007202846,0.00001719866],"domain_scores_gemma":[0.9998052,0.0000814257,0.00002424078,0.00003017809,0.00004648491,0.0000126022],"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":[0.0003104079,0.00006946515,0.0009778333,0.0001595943,0.00004656097,0.0001938313,0.00007789364,0.07671548,0.1230494,0.01630014,0.008975372,0.773124],"study_design_scores_gemma":[0.00000884609,0.00006429206,0.0009268467,0.00002199824,0.000008026668,0.000128747,0.00001866748,0.9450575,0.03637544,0.01013499,0.007242037,0.00001260743],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02890498,0.002125038,0.9604856,0.0003755186,0.0001493041,0.00006302851,0.0002443466,0.003785967,0.003866265],"genre_scores_gemma":[0.5047227,0.002865038,0.474547,0.0003674869,0.0002259226,0.0001267512,0.001099588,0.0004211044,0.0156244],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003194922,"threshold_uncertainty_score":0.01068813,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02856686301845925,"score_gpt":0.2949848587989118,"score_spread":0.2664179957804526,"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."}}