{"id":"W2949341414","doi":"10.48550/arxiv.1902.02777","title":"FDDB-360: Face Detection in 360-degree Fisheye Images","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Face recognition and analysis","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Degree (music); Computer vision; Computer science; Artificial intelligence; Face (sociological concept); Face detection; Cover (algebra); Detector; Facial recognition system; Pattern recognition (psychology); Engineering","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":[0.0009253166,0.002314358,0.001403759,0.002070464,0.0007645947,0.001040776,0.002680703,0.001884485,0.008989829],"category_scores_gemma":[0.002683461,0.0006992105,0.001480375,0.00149355,0.0005476217,0.001077548,0.001804536,0.001570239,0.01056948],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007922616,"about_ca_system_score_gemma":0.0008413474,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01659352,"about_ca_topic_score_gemma":0.02068926,"domain_scores_codex":[0.9986061,0.000142502,0.00007391965,0.0004930865,0.0004529384,0.0002314336],"domain_scores_gemma":[0.9989624,0.0001608633,0.00006537436,0.000514234,0.0002108427,0.00008623591],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002059228,0.001183106,0.01099129,0.001816588,0.0004974802,0.000480565,0.000150063,0.02518496,0.05037017,0.002425218,0.5170909,0.3877504],"study_design_scores_gemma":[0.000931046,0.001876429,0.07546516,0.0004352229,0.0002810375,0.006075006,0.0005247834,0.4171888,0.2050434,0.01351438,0.2782375,0.0004272411],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.2333877,0.003678701,0.1983267,0.001057047,0.001334241,0.001657531,0.4784403,0.05537055,0.02674725],"genre_scores_gemma":[0.1886451,0.0007729028,0.1706251,0.0004820166,0.0001307879,0.0008597863,0.6280023,0.0009151514,0.009566857],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01659352,"threshold_uncertainty_score":0.03299385,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07305437482540658,"score_gpt":0.1841208464334873,"score_spread":0.1110664716080807,"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."}}