{"id":"W3202312310","doi":"10.1109/iccv48922.2021.01076","title":"BV-Person: A Large-scale Dataset for Bird-view Person Re-identification","year":2021,"lang":"en","type":"article","venue":"2021 IEEE/CVF International Conference on Computer Vision (ICCV)","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Task (project management); Artificial intelligence; Identification (biology); Focus (optics); Object (grammar); Scale (ratio); Computer vision; Geography; Cartography","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":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.001359854,0.000373472,0.0004440339,0.0002610614,0.0003120916,0.001204894,0.001708885,0.0001641607,0.0005725099],"category_scores_gemma":[0.0001162896,0.0003678746,0.0002652923,0.0004910525,0.00004763685,0.0008754832,0.0002723947,0.0003381945,0.0005577636],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001486419,"about_ca_system_score_gemma":0.0002370634,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002889591,"about_ca_topic_score_gemma":0.0001266042,"domain_scores_codex":[0.9962212,0.0003970987,0.0005720195,0.001408843,0.0009063025,0.0004945713],"domain_scores_gemma":[0.9968141,0.0004139342,0.000331009,0.001214049,0.001038591,0.0001882767],"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":[0.0001998002,0.001531331,0.0007757765,0.0002061756,0.0004022398,0.0003286192,0.002630659,0.0009804077,0.01508291,0.0846448,0.2709508,0.6222665],"study_design_scores_gemma":[0.001696851,0.0003784273,0.003711005,0.0004797311,0.00002574496,0.0000849494,0.0002720517,0.8450854,0.007086696,0.002441842,0.137953,0.0007843411],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002374563,0.0001311344,0.9750081,0.01429472,0.00590098,0.0003539306,0.0007679605,0.0001915625,0.0009770847],"genre_scores_gemma":[0.5863251,0.0005249883,0.3958664,0.007054114,0.002558979,0.0002289767,0.003898366,0.0001161941,0.003426926],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8441049,"threshold_uncertainty_score":0.9998773,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1027138669387864,"score_gpt":0.3737466514381948,"score_spread":0.2710327844994084,"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."}}