{"id":"W3042721654","doi":"10.1109/icpr48806.2021.9413149","title":"Relatable Clothing: Detecting Visual Relationships between People and Clothing","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Clothing; Biometrics; Computer science; Artificial intelligence; Visibility; Segmentation; Field (mathematics); Computer vision; Pattern recognition (psychology); Geography; Mathematics","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":[0.0003203432,0.0009502745,0.0004754521,0.001095769,0.0002904457,0.0007170349,0.0007751306,0.0007511313,0.00303636],"category_scores_gemma":[0.0009033607,0.0003108208,0.0006868611,0.0006259042,0.0004547945,0.0005745186,0.0009988843,0.0008390273,0.002276148],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000276805,"about_ca_system_score_gemma":0.0001794022,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004922487,"about_ca_topic_score_gemma":0.01236816,"domain_scores_codex":[0.9994965,0.00007463682,0.00001478041,0.00020536,0.0001112347,0.00009743691],"domain_scores_gemma":[0.9996647,0.00005084436,0.00004781909,0.0001323071,0.00005953532,0.00004483448],"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":[0.002261422,0.0006873105,0.04075377,0.001072663,0.0004504533,0.001313068,0.0004962942,0.03505122,0.1655887,0.003044383,0.09489814,0.6543825],"study_design_scores_gemma":[0.000116308,0.0007969277,0.1393079,0.0003537752,0.0002054968,0.006389872,0.0008825919,0.6539137,0.1237054,0.00708213,0.06713578,0.0001100873],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7014917,0.004071807,0.2243678,0.0007404067,0.0006551659,0.0005401911,0.0279094,0.01074481,0.02947869],"genre_scores_gemma":[0.8550531,0.0009548813,0.08719486,0.0004855837,0.0001585441,0.0001072324,0.04296871,0.0005548297,0.01252238],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004922487,"threshold_uncertainty_score":0.01015759,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03989076297895446,"score_gpt":0.2646250684358539,"score_spread":0.2247343054568994,"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."}}