{"id":"W3033468485","doi":"10.3390/electronics9060924","title":"MDEAN: Multi-View Disparity Estimation with an Asymmetric Network","year":2020,"lang":"en","type":"article","venue":"Electronics","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Artificial intelligence; Computer science; Computer vision; Monocular; Convolutional neural network; Focus (optics); Deep learning; Matching (statistics); Contrast (vision); Set (abstract data type); Mathematics","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":[],"consensus_categories":[],"category_scores_codex":[0.0001274119,0.0001150333,0.0001308885,0.000030505,0.0001356396,0.0001174539,0.0004505119,0.0000241824,0.000004377618],"category_scores_gemma":[0.00005029984,0.00009629957,0.00002386043,0.001060391,0.00001562237,0.0007502044,0.00008897027,0.0002307916,0.00003761192],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004895517,"about_ca_system_score_gemma":0.00008906516,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001521018,"about_ca_topic_score_gemma":0.00001082706,"domain_scores_codex":[0.998935,0.00004881018,0.0001283521,0.000331774,0.0001872324,0.0003688742],"domain_scores_gemma":[0.9994105,0.00003127197,0.00006904241,0.0002925211,0.0000452668,0.0001514328],"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":[0.00001183522,0.0000451815,0.0003194879,0.00001025695,0.000006912889,0.000005071467,0.0001242239,0.01365963,0.00002034343,0.01732066,0.0003595044,0.9681169],"study_design_scores_gemma":[0.0002737543,0.0003031049,0.0008627732,0.00001056807,0.000004247322,0.000006197676,0.000002556261,0.9697568,0.0001802029,0.001091099,0.02736644,0.000142263],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0004085623,0.003791325,0.9935714,0.00162974,0.00004804788,0.0001142162,2.589544e-7,0.0002341441,0.0002022766],"genre_scores_gemma":[0.1630189,0.0003509881,0.8337381,0.002755347,0.000081891,0.000006107622,0.000006751629,0.00001396255,0.00002797904],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9679746,"threshold_uncertainty_score":0.3926983,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01935696343832977,"score_gpt":0.2812382109567473,"score_spread":0.2618812475184176,"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."}}