{"id":"W4220913778","doi":"10.1145/3501404","title":"Clustering Matters: Sphere Feature for Fully Unsupervised Person Re-identification","year":2022,"lang":"en","type":"article","venue":"ACM Transactions on Multimedia Computing Communications and Applications","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"Six Talent Peaks Project in Jiangsu Province; Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China","keywords":"Artificial intelligence; Cluster analysis; Unsupervised learning; Pattern recognition (psychology); Computer science; Feature (linguistics); Feature learning; Feature vector; Complete-linkage clustering; Artificial neural network; Machine learning; Correlation clustering; Canopy clustering algorithm","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","sts"],"consensus_categories":[],"category_scores_codex":[0.0008274765,0.0002152143,0.0002234864,0.0002066759,0.003209827,0.0002485514,0.002562362,0.00006988721,0.00002492151],"category_scores_gemma":[0.00003221647,0.0002493133,0.0001361973,0.0007688046,0.0001124492,0.0002364606,0.0002103372,0.0005170294,0.00001209721],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001063657,"about_ca_system_score_gemma":0.00006932281,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000492549,"about_ca_topic_score_gemma":0.00005383755,"domain_scores_codex":[0.998207,0.0003018897,0.0003565092,0.0005931145,0.0002396602,0.0003017972],"domain_scores_gemma":[0.9948878,0.001506663,0.0002056891,0.003138496,0.0001516667,0.0001096577],"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":[0.00001346803,0.0003592768,0.00008685286,0.00004377677,0.00006354485,3.204901e-7,0.00222454,0.008131742,0.001830893,0.001198228,0.0004767393,0.9855706],"study_design_scores_gemma":[0.0009775506,0.0001291778,0.00228199,0.00003162186,0.00005351736,0.00003089457,0.001914212,0.9131395,0.0002955344,0.001329593,0.07934549,0.0004708858],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0004751379,0.000351375,0.9718295,0.02545006,0.0001833816,0.001113678,0.00009343092,0.0003728378,0.0001305658],"genre_scores_gemma":[0.4361629,0.0001070791,0.5612956,0.0007739962,0.0000480965,0.001322531,0.000098043,0.00002631659,0.0001654714],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9850997,"threshold_uncertainty_score":0.9999959,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05064696583485039,"score_gpt":0.3158366727462122,"score_spread":0.2651897069113618,"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."}}