{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002170774,0.0005111587,0.0003254144,0.0006285905,0.0001523564,0.0004481814,0.0004750825,0.0003739984,0.003213868],"category_scores_gemma":[0.001153344,0.0002483331,0.0002509867,0.0005791368,0.0002443362,0.0008270736,0.0005918883,0.000729995,0.001263995],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004376881,"about_ca_system_score_gemma":0.0003223667,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002897001,"about_ca_topic_score_gemma":0.002397345,"domain_scores_codex":[0.9998308,0.00002149804,0.000006540882,0.00004383819,0.00007943191,0.00001785462],"domain_scores_gemma":[0.9998116,0.00007715105,0.00002387663,0.00002887872,0.00004639358,0.00001217172],"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":[0.0003018178,0.00006906159,0.000927056,0.0001546731,0.00004325635,0.0001985164,0.00007944612,0.06915237,0.1353195,0.01321834,0.007876756,0.7726592],"study_design_scores_gemma":[0.000009590079,0.00008010637,0.00108617,0.00002559292,0.000008978203,0.000166711,0.00002267515,0.9343485,0.04664496,0.009214493,0.008377694,0.00001453251],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02897346,0.001993301,0.9607477,0.0003168923,0.0001365568,0.00006532158,0.0002321417,0.003909203,0.0036254],"genre_scores_gemma":[0.5070301,0.002804526,0.4723149,0.0003467893,0.0002137284,0.000122589,0.001118965,0.0004349684,0.01561337],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003213868,"threshold_uncertainty_score":0.01075149,"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."}}