{"id":"W4287712192","doi":"10.48550/arxiv.2007.10283","title":"Relatable Clothing: Detecting Visual Relationships between People and\\n Clothing","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Textile materials and evaluations","field":"Materials Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Ministère de la Défense Nationale","keywords":"Clothing; Biometrics; Computer science; Artificial intelligence; Visibility; Segmentation; Field (mathematics); Computer vision; Pattern recognition (psychology); Geography; Mathematics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004447469,0.001106382,0.000452187,0.001481859,0.0003452137,0.0009361575,0.0009544429,0.0009577989,0.00652906],"category_scores_gemma":[0.00112544,0.000319668,0.0006823833,0.0008044722,0.0003878488,0.001002057,0.001289534,0.0006497555,0.003344046],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004035325,"about_ca_system_score_gemma":0.0002412864,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008163465,"about_ca_topic_score_gemma":0.02106624,"domain_scores_codex":[0.9994698,0.00007274161,0.00001673001,0.0002219159,0.0001246522,0.00009419507],"domain_scores_gemma":[0.9996452,0.000064113,0.00004459158,0.0001182186,0.00007917095,0.00004867872],"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":[0.001504434,0.000538086,0.02368463,0.0007953168,0.0002479766,0.0004790443,0.0002712999,0.01425373,0.06662553,0.00216382,0.07898912,0.8104469],"study_design_scores_gemma":[0.0001115992,0.001098977,0.1390973,0.0004151967,0.0002413949,0.003108087,0.001136103,0.650744,0.1109582,0.008254059,0.08473217,0.0001028602],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6567995,0.005719339,0.2366341,0.001011429,0.0008678794,0.0005760967,0.02159922,0.01720315,0.05958925],"genre_scores_gemma":[0.8287598,0.001298668,0.1061083,0.0005262863,0.0001525688,0.0001307781,0.04128367,0.0005335931,0.02120624],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008163465,"threshold_uncertainty_score":0.02184188,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1820676502562259,"score_gpt":0.2365317614180815,"score_spread":0.05446411116185565,"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."}}