{"id":"W4400391233","doi":"10.1016/j.imavis.2024.105160","title":"GDM-depth: Leveraging global dependency modelling for self-supervised indoor depth estimation","year":2024,"lang":"en","type":"article","venue":"Image and Vision Computing","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ottawa","funders":"Ministry of Education of the People's Republic of China; National Natural Science Foundation of China","keywords":"Computer science; Artificial intelligence; Ground truth; Feature (linguistics); Unary operation; Data mining; Machine learning; Pattern recognition (psychology)","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":[0.0005308451,0.001391846,0.001397718,0.0009117338,0.0003633849,0.0006750211,0.002758135,0.001135375,0.003313282],"category_scores_gemma":[0.001888456,0.0008502234,0.001078323,0.001137565,0.0004141489,0.001468494,0.002151094,0.00193963,0.001964105],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005258968,"about_ca_system_score_gemma":0.001234863,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01183535,"about_ca_topic_score_gemma":0.03030755,"domain_scores_codex":[0.9994614,0.00007171959,0.0000192023,0.0001838597,0.000189778,0.00007403871],"domain_scores_gemma":[0.9994339,0.0001615766,0.00005494399,0.0001576415,0.0001531201,0.0000388327],"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":[0.0002863374,0.0002886775,0.002085756,0.0001763889,0.0002113127,0.0001056613,0.0001218745,0.2460348,0.02949059,0.003502455,0.01458908,0.7031069],"study_design_scores_gemma":[0.000007053979,0.00001701473,0.0002459169,0.000004134035,0.000007653919,0.00002426972,0.000006240771,0.9941737,0.003420763,0.001186699,0.0008991963,0.00000740833],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00807492,0.0001729339,0.9862258,0.00005948187,0.0000454349,0.00003895273,0.0003116338,0.004532755,0.0005379707],"genre_scores_gemma":[0.2482836,0.0002590718,0.7438859,0.0002652906,0.00009630913,0.0001502163,0.00226907,0.00101216,0.003778317],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01183535,"threshold_uncertainty_score":0.02353287,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02517563298204604,"score_gpt":0.3412107901119456,"score_spread":0.3160351571298995,"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."}}