{"id":"W4293868161","doi":"10.1109/crv55824.2022.00030","title":"Improving tracking with a tracklet associator","year":2022,"lang":"en","type":"article","venue":"","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Constraint (computer-aided design); Computer science; BitTorrent tracker; Tracking (education); Task (project management); Object (grammar); Identity (music); Artificial intelligence; Association (psychology); Computer vision; Position (finance); Constraint programming; Video tracking; Interpolation (computer graphics); Local consistency; Base (topology); Eye tracking; Image (mathematics); Mathematics; Mathematical optimization; Constraint satisfaction problem","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":[0.0002565552,0.00008299867,0.00009791064,0.000059293,0.0002854602,0.0001050213,0.0005612561,0.00001337675,0.00006953609],"category_scores_gemma":[0.0000291622,0.00006834606,0.00003567186,0.0004255661,0.0000129132,0.0006806292,0.000301191,0.0001963116,0.000004158089],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009166537,"about_ca_system_score_gemma":0.00006064686,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002192429,"about_ca_topic_score_gemma":0.000002567615,"domain_scores_codex":[0.9990661,0.0000401996,0.000109686,0.0002633382,0.0003028656,0.0002178398],"domain_scores_gemma":[0.9995041,0.0000519308,0.00007692635,0.0002812593,0.00004232251,0.00004338322],"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":[0.00002364168,0.0001576745,0.001464984,0.0000125108,0.00002333164,0.0001702313,0.0007616749,0.00004937674,0.01447086,0.05499988,0.002387826,0.925478],"study_design_scores_gemma":[0.002846749,0.004478237,0.005369468,0.00003603092,0.00004184703,0.0006818805,0.001359782,0.05799491,0.6811393,0.0305695,0.2129419,0.002540395],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003017111,0.00006131524,0.9924743,0.0003716608,0.00004935315,0.0001191891,0.000001590115,0.0006616586,0.003243786],"genre_scores_gemma":[0.7629186,0.000002535345,0.2349271,0.001027709,0.00002531904,0.00004444594,9.469147e-7,0.00001111571,0.001042204],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9229376,"threshold_uncertainty_score":0.2787071,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0112185814276037,"score_gpt":0.2406220898319027,"score_spread":0.229403508404299,"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."}}