{"id":"W2952447184","doi":"10.48550/arxiv.1808.07349","title":"Multi-Branch Siamese Networks with Online Selection for Object Tracking","year":2018,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université TÉLUQ; Polytechnique Montréal","funders":"","keywords":"BitTorrent tracker; Artificial intelligence; Computer science; Video tracking; Tracking (education); Object (grammar); Representation (politics); Selection (genetic algorithm); Computer vision; Pattern recognition (psychology); Eye tracking","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":[0.001629946,0.001294667,0.001112621,0.000768795,0.0006447748,0.0009592372,0.001750477,0.001324319,0.003391385],"category_scores_gemma":[0.003778964,0.0005448327,0.000615976,0.0008231496,0.00073953,0.002124663,0.001476499,0.001729789,0.001205784],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008723372,"about_ca_system_score_gemma":0.001175511,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003566516,"about_ca_topic_score_gemma":0.006451757,"domain_scores_codex":[0.9994336,0.0001187981,0.00002765996,0.0001979818,0.0001445972,0.00007737878],"domain_scores_gemma":[0.9987395,0.0005367519,0.0001342904,0.0001741425,0.0003047013,0.0001106119],"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":[0.0003901528,0.0001837127,0.003100759,0.00006713119,0.0001012294,0.0001658894,0.0001356822,0.463892,0.01789843,0.01701113,0.005814094,0.4912398],"study_design_scores_gemma":[0.000006936062,0.00002860677,0.0001090644,0.00000247786,0.000006117283,0.00001884342,0.000003599095,0.9951653,0.001526991,0.002749186,0.0003788899,0.000003906952],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02223646,0.0003069585,0.9744435,0.0001328303,0.00003962117,0.00004827935,0.00005479148,0.001147445,0.001590009],"genre_scores_gemma":[0.600469,0.0003783505,0.3877497,0.0004117107,0.0001352068,0.0002644256,0.0006520773,0.0003509835,0.009588577],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003566516,"threshold_uncertainty_score":0.01134533,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1089052737029499,"score_gpt":0.2449333834944978,"score_spread":0.1360281097915479,"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."}}