{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002460076,0.0003071529,0.0003855195,0.0006520298,0.00009353503,0.0001838709,0.001346461,0.00031532,0.00008600153],"category_scores_gemma":[0.00004130791,0.0003619838,0.0003064957,0.001025415,0.00006093828,0.0005457829,0.001250973,0.0006850597,0.0006968586],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002631845,"about_ca_system_score_gemma":0.0001173804,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000432325,"about_ca_topic_score_gemma":0.0004345412,"domain_scores_codex":[0.9979668,0.0001804355,0.000220764,0.001155624,0.0001203924,0.000355988],"domain_scores_gemma":[0.9984074,0.00009002852,0.000218092,0.001022404,0.0001346863,0.0001273387],"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":[0.0001010353,0.0006634527,0.02625236,0.000529957,0.0005444711,0.001234736,0.00113899,0.8372519,0.001861492,0.0073314,0.001385811,0.1217044],"study_design_scores_gemma":[0.0008670557,0.00004794975,0.008364797,0.0001953718,0.00009187402,0.000006023169,0.0002565024,0.9764571,0.003043784,0.009024605,0.0007906248,0.000854341],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2138441,0.00008125211,0.7779502,0.0002246415,0.0005721154,0.000263403,0.00001665991,0.0002471218,0.006800445],"genre_scores_gemma":[0.9932946,0.0003695807,0.0006000495,0.0001073224,0.00003659282,0.000001104404,0.00001187156,0.0000143741,0.005564531],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7794505,"threshold_uncertainty_score":0.9998832,"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."}}