{"id":"W4396604587","doi":"10.1109/iccv51701.2025.01613","title":"Streamlining Image Editing with Layered Diffusion Brushes","year":2025,"lang":"en","type":"preprint","venue":"","topic":"Nanoporous metals and alloys","field":"Materials Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Image editing; Image (mathematics); Diffusion; Computer science; Computer graphics (images); Computer vision; Physics","routes":{"ca_aff":true,"ca_fund":true,"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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004209925,0.0003305408,0.0004831374,0.0001029678,0.0001697079,0.0003402139,0.0004625031,0.0001833064,0.001418565],"category_scores_gemma":[0.00008491527,0.0002296175,0.00009458025,0.0001015079,0.00007924481,0.000110529,0.001158593,0.0003201601,0.00008836055],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003933775,"about_ca_system_score_gemma":0.0002022591,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001034644,"about_ca_topic_score_gemma":0.0001866476,"domain_scores_codex":[0.9980973,0.00007365928,0.0003893765,0.0007037359,0.0003658281,0.00037009],"domain_scores_gemma":[0.9988612,0.0001074775,0.0002720595,0.0005454333,0.0001302905,0.00008355638],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00007160898,0.0002110982,0.001448386,0.0007007627,0.00005569559,0.00009523232,0.0006033546,0.0007386533,0.9834771,0.000843564,0.003390215,0.008364297],"study_design_scores_gemma":[0.001140321,0.0001619002,0.001039151,0.002486699,0.0001723584,0.00001395943,0.001232513,0.002132753,0.9832561,0.00232163,0.004816689,0.001225941],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.927924,0.0001163723,0.007413271,0.0002683095,0.00152835,0.0003390254,0.00009029057,0.0004030606,0.06191725],"genre_scores_gemma":[0.900794,0.0000894753,0.08659041,0.0002165372,0.000809467,0.00005663862,0.00007486378,0.00003764703,0.01133096],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07917714,"threshold_uncertainty_score":0.9994943,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01275480978649239,"score_gpt":0.2547100203164266,"score_spread":0.2419552105299343,"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."}}