{"id":"W4247236555","doi":"10.32920/ryerson.14644374","title":"Adaptive Depth Guided Image Completion for Structure and Texture Synthesis","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University; Toronto Metropolitan University","funders":"","keywords":"Inpainting; Texture synthesis; Artificial intelligence; Image (mathematics); Coherence (philosophical gambling strategy); Computer science; Computer vision; Process (computing); Matching (statistics); Mathematics; Image processing; Algorithm; Image texture","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":[0.0003775652,0.0005019887,0.000437205,0.0004296037,0.0001713482,0.0004066905,0.0005816541,0.0005313288,0.001778121],"category_scores_gemma":[0.001033095,0.0002819031,0.0005275988,0.0003021133,0.0004701485,0.0005984329,0.0006737184,0.0009171964,0.0004027241],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004508155,"about_ca_system_score_gemma":0.0004956675,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001079088,"about_ca_topic_score_gemma":0.001362562,"domain_scores_codex":[0.9997447,0.00003804748,0.000009456156,0.00005164469,0.0001335697,0.00002253005],"domain_scores_gemma":[0.9996628,0.0001383831,0.00004837917,0.00006483246,0.00006123257,0.0000244539],"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":[0.0002311303,0.0001182379,0.0003673529,0.0003119965,0.00005523408,0.0001402338,0.0002569472,0.3222021,0.3581159,0.02869758,0.002332682,0.2871706],"study_design_scores_gemma":[0.00002042083,0.00008411273,0.0001847395,0.000008823345,0.000009164805,0.0001386525,0.00001612185,0.9500015,0.04096851,0.005146325,0.003406132,0.00001551416],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009111536,0.000148328,0.989803,0.00003505849,0.00001352339,0.00002331738,0.00002118228,0.0001937175,0.0006501902],"genre_scores_gemma":[0.1959801,0.0004157459,0.7998656,0.00005060599,0.00004431733,0.000063616,0.0001296727,0.0001600748,0.003290222],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001778121,"threshold_uncertainty_score":0.005948424,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03639960338871221,"score_gpt":0.3085790880445509,"score_spread":0.2721794846558386,"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."}}