{"id":"W4407390521","doi":"10.1109/jiot.2025.3540917","title":"Exploiting the Potential of Self-Supervised Monocular Depth Estimation via Patch-Based Self-Distillation","year":2025,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Image Processing Techniques and Applications","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University; University of British Columbia","funders":"National Natural Science Foundation of China-Shandong Joint Fund; National Natural Science Foundation of China","keywords":"Computer science; Monocular; Estimation; Artificial intelligence; Distillation; Computer vision; Pattern recognition (psychology); Chemistry","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":[0.00031836,0.0001101967,0.0001467328,0.0001215825,0.00007343869,0.0000713694,0.0002764797,0.00006346083,0.00001004555],"category_scores_gemma":[0.00002304566,0.0000906826,0.0001049768,0.0001566943,0.00002689716,0.0002291914,0.00001949124,0.0002510834,0.000001338312],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006881837,"about_ca_system_score_gemma":0.00003445411,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004036471,"about_ca_topic_score_gemma":8.193933e-7,"domain_scores_codex":[0.9991508,0.00002653605,0.0004434236,0.00008422643,0.0001742847,0.0001207104],"domain_scores_gemma":[0.9994142,0.00003877714,0.0001997405,0.0001437124,0.000178135,0.00002542578],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008046114,0.0003611913,0.001735249,0.00160725,0.0006629399,0.00001313677,0.006228331,0.309002,0.3588941,0.0008226969,0.004304141,0.3162885],"study_design_scores_gemma":[0.0001736295,0.000017091,0.0001615275,0.0002095239,0.00006226121,0.00001554774,0.00003669679,0.8372213,0.159853,0.002109392,0.00007522174,0.00006485228],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1747193,0.0001188813,0.8241541,0.000147555,0.0001528411,0.0001009508,8.422168e-7,0.0002096342,0.0003958774],"genre_scores_gemma":[0.8185842,0.00001598393,0.1812852,0.00004392486,0.00003525128,0.000009102108,0.000002212781,0.00001416426,0.000009908786],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6438649,"threshold_uncertainty_score":0.3697929,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007252563614717386,"score_gpt":0.2371454751526614,"score_spread":0.229892911537944,"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."}}