{"id":"W4386392998","doi":"10.1007/s00521-023-08913-2","title":"Vision transformer with multiple granularities for person re-identification","year":2023,"lang":"en","type":"article","venue":"Neural Computing and Applications","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"National Natural Science Foundation of China","keywords":"Granularity; Discriminative model; Computer science; Transformer; Artificial intelligence; Benchmark (surveying); Pattern recognition (psychology); Feature extraction; Machine learning; Voltage; Engineering","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":[0.0007788898,0.0005840492,0.001205134,0.001257,0.0003715517,0.001100342,0.00114944,0.0009438742,0.003730451],"category_scores_gemma":[0.001918852,0.0003730648,0.0008198177,0.001441173,0.0004924548,0.002298651,0.00182528,0.001286737,0.001612918],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006149169,"about_ca_system_score_gemma":0.000798229,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005419582,"about_ca_topic_score_gemma":0.005143297,"domain_scores_codex":[0.9993291,0.00007620612,0.00003659312,0.0002192493,0.0002070956,0.0001318304],"domain_scores_gemma":[0.9994301,0.000120116,0.00004256742,0.0002386403,0.0001215656,0.00004706022],"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":[0.0008847298,0.0003273001,0.001644079,0.0001547095,0.0001324445,0.0001927498,0.00007317714,0.0460265,0.05798562,0.01600818,0.00548927,0.8710812],"study_design_scores_gemma":[0.00002572129,0.0001080742,0.001543018,0.00001904697,0.00005476064,0.0002681176,0.00004770631,0.9543507,0.0261916,0.01482568,0.00253996,0.00002570526],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02548152,0.0004830051,0.9694344,0.0001324233,0.0001167192,0.0000649124,0.0002158996,0.001666536,0.002404574],"genre_scores_gemma":[0.7716102,0.0005460536,0.2214117,0.0002310522,0.000095211,0.00005835332,0.0005716624,0.0001426759,0.005333112],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005419582,"threshold_uncertainty_score":0.0124796,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03952398071804249,"score_gpt":0.3236776671711854,"score_spread":0.2841536864531429,"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."}}