{"id":"W3157630927","doi":"10.32920/22734374.v1","title":"Motion Vector Extrapolation for Video Object Detection","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Artificial intelligence; Computer vision; Object detection; Latency (audio); Motion vector; Optical flow; Motion estimation; Low latency (capital markets); Benchmark (surveying); Pattern recognition (psychology)","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":[0.0002025865,0.0002056851,0.0001745046,0.0001716856,0.0001893964,0.0001553194,0.0006397882,0.0001677408,0.00000412352],"category_scores_gemma":[0.0000779462,0.0002134981,0.000148661,0.0004135236,0.0000169084,0.0002966803,0.0004929766,0.0002677526,0.00008722466],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001468937,"about_ca_system_score_gemma":0.00004387633,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003231781,"about_ca_topic_score_gemma":0.0001187779,"domain_scores_codex":[0.9983906,0.00003805569,0.0003075846,0.0008170612,0.0001954654,0.000251265],"domain_scores_gemma":[0.998412,0.0002685006,0.0002269538,0.000885751,0.000144874,0.00006196212],"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":[0.0000329291,0.0001136231,0.00008818755,0.0002875722,0.00008644626,0.000002509871,0.0003157303,0.1723441,0.02754144,0.1248866,0.004082993,0.6702179],"study_design_scores_gemma":[0.000134426,0.00003644015,0.002291362,0.00002321086,0.00001226127,0.000002925389,0.000003315546,0.8165655,0.01241616,0.1658832,0.002357454,0.000273742],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0007933312,0.00003200873,0.9934002,0.0009782596,0.00156668,0.001355523,0.00001135551,0.00167167,0.0001909729],"genre_scores_gemma":[0.7541921,0.00003091813,0.2417992,0.0001759209,0.0006428367,0.001968887,0.00008178607,0.0000521759,0.001056179],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7533988,"threshold_uncertainty_score":0.87062,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05585239527241653,"score_gpt":0.3089934045694444,"score_spread":0.2531410092970279,"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."}}