{"id":"W4390190318","doi":"10.1109/iccvw60793.2023.00089","title":"IDTransformer: Transformer for Intrinsic Image Decomposition","year":2023,"lang":"en","type":"article","venue":"","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Invariant (physics); Computer science; Artificial intelligence; Shading; Prior probability; Computer vision; Photometric stereo; Transformer; Algorithm; Image (mathematics); Mathematics; Physics; Computer graphics (images)","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":[0.0002754821,0.0001131017,0.000109683,0.0001871643,0.0001140699,0.0001200228,0.0004616694,0.00004112618,0.00004064199],"category_scores_gemma":[0.000007272416,0.0001024167,0.00009211561,0.0005219312,0.00002603275,0.001023591,0.00002417068,0.0000553575,0.0002537573],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003253768,"about_ca_system_score_gemma":0.00002607905,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006758598,"about_ca_topic_score_gemma":0.000006349452,"domain_scores_codex":[0.9990242,0.00001254708,0.0001905873,0.0002740868,0.000164637,0.0003339957],"domain_scores_gemma":[0.9995383,0.00005875872,0.00002196707,0.0002624167,0.00007136485,0.00004720443],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00001691231,0.0000600476,0.00001344482,0.00006689216,0.0000208266,0.000007583186,0.0004747564,0.000002128535,0.3093571,0.1184459,0.04899333,0.522541],"study_design_scores_gemma":[0.0004746081,0.0001920224,0.0003140284,0.00001524017,0.000006348019,0.000005813921,0.00001665924,0.01237059,0.9388329,0.02104046,0.02648687,0.0002444497],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001912681,0.000008120683,0.9752468,0.002681065,0.0001743621,0.0005741579,0.000003753338,0.002091259,0.01730778],"genre_scores_gemma":[0.3099685,0.0001064552,0.685463,0.001029712,0.00009551339,0.0005253254,0.00004662179,0.00002879924,0.002736114],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.6294758,"threshold_uncertainty_score":0.4176431,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01350195557272324,"score_gpt":0.3078330362548136,"score_spread":0.2943310806820903,"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."}}