{"id":"W3090949667","doi":"10.1109/icip40778.2020.9190957","title":"Reliable Temporally Consistent Feature Adaptation for Visual Object Tracking","year":2020,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Artificial intelligence; BitTorrent tracker; Video tracking; Reliability (semiconductor); Feature (linguistics); Tracking (education); Adaptation (eye); Consistency (knowledge bases); Pattern recognition (psychology); Eye tracking; Object (grammar); Computer vision; Filter (signal processing); Machine learning","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":[0.001843177,0.0009747499,0.001122507,0.001070684,0.0003895143,0.0006898738,0.001388333,0.0008951771,0.0008523554],"category_scores_gemma":[0.007296417,0.000498973,0.0007017145,0.001857005,0.0005156541,0.001229634,0.0009108859,0.001567762,0.0007744395],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006915385,"about_ca_system_score_gemma":0.001102254,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006601472,"about_ca_topic_score_gemma":0.006227111,"domain_scores_codex":[0.9988206,0.0002320748,0.00004925027,0.0004117968,0.0003953871,0.0000907725],"domain_scores_gemma":[0.9978985,0.0007457284,0.0003128726,0.0004970304,0.0004814927,0.00006438689],"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":[0.0003382348,0.0001280395,0.003023896,0.0001200144,0.0001483397,0.0001203374,0.0001275947,0.3798349,0.03308728,0.006016086,0.01001961,0.5670357],"study_design_scores_gemma":[0.00001030276,0.00003067662,0.0007409353,0.000005603516,0.00001282823,0.00004913174,0.000005565059,0.9921858,0.003384063,0.002431155,0.001131986,0.00001188068],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007277432,0.0003111302,0.9907697,0.0000412375,0.00003062962,0.00001742047,0.00008518726,0.001132991,0.0003342906],"genre_scores_gemma":[0.5389142,0.0006226157,0.4556276,0.0002337819,0.000197582,0.000183444,0.001289563,0.0005447129,0.002386405],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006601472,"threshold_uncertainty_score":0.01312613,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0731385545903369,"score_gpt":0.3181389833149078,"score_spread":0.2450004287245708,"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."}}