{"id":"W4212826194","doi":"10.1109/jiot.2022.3151374","title":"Lightweight Monocular Depth Estimation on Edge Devices","year":2022,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"St. Francis Xavier University","funders":"National Natural Science Foundation of China","keywords":"Computer science; Encoder; Pruning; Enhanced Data Rates for GSM Evolution; Monocular; Upsampling; Computational complexity theory; Artificial intelligence; Computer engineering; Computer vision; Algorithm; Image (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":[0.0004529614,0.0001079707,0.0001462604,0.0002182032,0.0001792955,0.0001655348,0.00109526,0.00001706442,0.00008792619],"category_scores_gemma":[0.00003847053,0.00009365561,0.0001048444,0.0001738062,0.00002038346,0.001093899,0.0002215314,0.0004760905,0.00002452413],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001014339,"about_ca_system_score_gemma":0.00003838197,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009182491,"about_ca_topic_score_gemma":3.133511e-7,"domain_scores_codex":[0.9987154,0.0000876636,0.0003316614,0.0001903988,0.0004981663,0.0001767154],"domain_scores_gemma":[0.999199,0.00005740075,0.0003676041,0.0002160226,0.00007619691,0.00008371308],"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":[0.00008296134,0.0003330164,0.0008273826,0.00003072039,0.0000977225,0.0002469968,0.009399421,0.01661775,0.009473993,0.01046873,0.02104131,0.93138],"study_design_scores_gemma":[0.000375164,0.0003353609,0.0002896936,0.0001089675,0.000006897989,0.0005562021,0.00007177576,0.9303087,0.04104637,0.007093337,0.01964135,0.0001661929],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03230493,0.0002101188,0.9623478,0.001095899,0.001607463,0.00005044672,3.475024e-7,0.0000513648,0.002331619],"genre_scores_gemma":[0.8562123,0.000008688621,0.141883,0.001416282,0.00006021116,0.00000272909,4.315253e-7,0.00000901952,0.0004073569],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9312138,"threshold_uncertainty_score":0.3819165,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01743491516282089,"score_gpt":0.2813025027585311,"score_spread":0.2638675875957102,"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."}}