{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001249913,0.0003365797,0.0005097049,0.0001674008,0.0008356416,0.0003466363,0.0004856738,0.0004349383,0.0003334902],"category_scores_gemma":[0.0005751588,0.0004023416,0.0001177814,0.0003587347,0.0001015895,0.0004702884,0.001461236,0.0008873392,0.0003933057],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001936615,"about_ca_system_score_gemma":0.0001805654,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006422864,"about_ca_topic_score_gemma":0.0001256737,"domain_scores_codex":[0.9974191,0.0005226529,0.0004030587,0.001097639,0.0001485588,0.0004089793],"domain_scores_gemma":[0.9982553,0.0004520834,0.0004259485,0.0004892349,0.0001179751,0.0002594541],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004048664,0.0002371918,0.3378304,0.002141324,0.0004550637,0.0003279817,0.02402976,0.3818952,0.1564511,0.09421183,0.0009407066,0.001074654],"study_design_scores_gemma":[0.003961969,0.0005101846,0.2611038,0.001523415,0.00298761,0.00004997706,0.007555279,0.4411432,0.02610674,0.2469641,0.00271063,0.00538304],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9639354,0.00005915034,0.03348217,0.0002127774,0.0006499353,0.0003612358,0.00008341412,0.0004238203,0.0007920348],"genre_scores_gemma":[0.9978552,0.00004403481,0.001320447,0.00003511716,0.0003301612,0.00000257233,0.00005474815,0.00005007107,0.0003076297],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1527523,"threshold_uncertainty_score":0.9998428,"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."}}