{"id":"W4313128123","doi":"10.1109/tvcg.2022.3226689","title":"SAC-GAN: Structure-Aware Image Composition","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Visualization and Computer Graphics","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Computer science; Artificial intelligence; Computer vision; Discriminator; Ground truth; Pattern recognition (psychology)","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":[],"consensus_categories":[],"category_scores_codex":[0.0001255762,0.0001886823,0.000168188,0.000278615,0.001068203,0.000246743,0.0003078065,0.00004814223,0.00008975246],"category_scores_gemma":[3.687735e-7,0.0001997235,0.00008814171,0.0007500911,0.00006280396,0.0004196234,0.00001204526,0.0002216451,0.000003171198],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003672227,"about_ca_system_score_gemma":0.00003077366,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001739069,"about_ca_topic_score_gemma":0.000008982602,"domain_scores_codex":[0.9985275,0.0002742234,0.000231944,0.0004305897,0.0003377601,0.0001979406],"domain_scores_gemma":[0.9993707,0.00006467654,0.00008760852,0.0002800963,0.0001035054,0.00009339089],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001277838,0.001289727,0.00007497764,0.00008031646,0.0003264275,0.00004912953,0.004490718,0.2560893,0.003692722,0.6486161,0.005134194,0.08002866],"study_design_scores_gemma":[0.0003953422,0.0002470311,0.0001261923,0.000007813815,0.00001762179,0.0000322632,0.00003203263,0.9904701,0.00586387,0.0008029439,0.001767839,0.0002369763],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002970041,0.00002537491,0.9954729,0.0001478008,0.0009313252,0.0001892888,0.00004388686,0.0001863853,0.00003296334],"genre_scores_gemma":[0.993441,0.00004462618,0.004657823,0.001689571,0.00007567252,0.00002346217,0.00002219991,0.00001674895,0.00002886096],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9908151,"threshold_uncertainty_score":0.821586,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01033326547828686,"score_gpt":0.2376525342348464,"score_spread":0.2273192687565596,"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."}}