{"id":"W3043306352","doi":"10.1109/tip.2020.3007843","title":"An Object Context Integrated Network for Joint Learning of Depth and Optical Flow","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"Fundamental Research Funds for the Central Universities; Natural Science Foundation of Heilongjiang Province; National Natural Science Foundation of China","keywords":"Exploit; Computer science; Artificial intelligence; Context (archaeology); Optical flow; Pyramid (geometry); Object (grammar); Joint (building); Deep learning; Context model; Depth map; Unsupervised learning; Pattern recognition (psychology); Flow (mathematics); Machine learning; Computer vision; Image (mathematics); Mathematics; 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":[0.0004463617,0.001334235,0.0007611869,0.0007753732,0.0003815957,0.000503355,0.001591141,0.001046037,0.001785149],"category_scores_gemma":[0.001059553,0.0005978613,0.0006798465,0.0008633355,0.0004213296,0.001575086,0.001531927,0.001477051,0.0003991866],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001073249,"about_ca_system_score_gemma":0.001266014,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0149228,"about_ca_topic_score_gemma":0.02487936,"domain_scores_codex":[0.9997143,0.00002924578,0.000009987607,0.0001325078,0.00006246429,0.00005142413],"domain_scores_gemma":[0.9998289,0.00004303607,0.00002461629,0.00002840049,0.00005502082,0.00001984704],"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":[0.0003568625,0.0002678766,0.002910778,0.0001376427,0.0001975752,0.000190377,0.0000925959,0.3759856,0.02814902,0.008532747,0.007731016,0.575448],"study_design_scores_gemma":[0.00000883737,0.00002906211,0.0002797658,0.000006064048,0.0000177507,0.0000213251,0.000004820307,0.994138,0.002590389,0.002227801,0.0006695204,0.000006660981],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0408427,0.001165177,0.9521258,0.0002754998,0.0001428705,0.0000848649,0.0004653611,0.002328021,0.002569826],"genre_scores_gemma":[0.6556345,0.0008596174,0.3340702,0.0004837317,0.0001560654,0.0002069145,0.001692504,0.0001712865,0.006725106],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0149228,"threshold_uncertainty_score":0.02967185,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02675466751288624,"score_gpt":0.2883348237121275,"score_spread":0.2615801561992412,"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."}}