{"id":"W4386065723","doi":"10.1109/cvpr52729.2023.01672","title":"Look, Radiate, and Learn: Self-Supervised Localisation via Radio-Visual Correspondence","year":2023,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Bell (Canada)","funders":"","keywords":"Computer science; Benchmark (surveying); Scalability; Key (lock); Perception; Artificial intelligence; Radio frequency; Deep learning; Telecommunications","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":[0.001052315,0.002337375,0.001021555,0.001153044,0.000656901,0.001298118,0.003893837,0.00203536,0.003742313],"category_scores_gemma":[0.003840854,0.0006036827,0.001470893,0.001150059,0.001048446,0.001551744,0.002338735,0.002491833,0.004067761],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001111676,"about_ca_system_score_gemma":0.000926855,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01319443,"about_ca_topic_score_gemma":0.02436751,"domain_scores_codex":[0.99897,0.0002861069,0.00004390375,0.0004194854,0.0001823111,0.00009811374],"domain_scores_gemma":[0.9987156,0.0004419067,0.0001279362,0.0003821387,0.0002304516,0.0001018233],"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":[0.001000355,0.0007598056,0.0110591,0.001073147,0.0004101108,0.0003887382,0.0002981095,0.4851204,0.009186957,0.00415856,0.1414633,0.3450814],"study_design_scores_gemma":[0.0001324424,0.0002284667,0.002342124,0.0000873005,0.00004157475,0.0002057172,0.0001467781,0.9669482,0.00821863,0.007722872,0.01387771,0.00004828416],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2794296,0.005515207,0.5905206,0.00340962,0.001491805,0.0006840441,0.05123888,0.04674963,0.02096049],"genre_scores_gemma":[0.6277483,0.000676913,0.2438199,0.001292056,0.0002212917,0.0003881443,0.1093007,0.001325316,0.01522739],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01319443,"threshold_uncertainty_score":0.02623528,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008829105122473983,"score_gpt":0.2159860347496665,"score_spread":0.2071569296271926,"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."}}