{"id":"W4407901240","doi":"10.1016/j.patcog.2025.111491","title":"Refining attention weights for facial super-resolution with counterfactual attention learning","year":2025,"lang":"en","type":"article","venue":"Pattern Recognition","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Counterfactual thinking; Refining (metallurgy); Computer science; Artificial intelligence; Resolution (logic); Face (sociological concept); Pattern recognition (psychology); Computer vision; Psychology; Social psychology; Chemistry; Linguistics","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":[0.001862198,0.00102758,0.0007737984,0.0006713098,0.0003341613,0.0006823735,0.001699326,0.001009935,0.001703344],"category_scores_gemma":[0.005999647,0.0004296772,0.0006387036,0.0004404192,0.0009055919,0.001282939,0.001542937,0.001634456,0.0003781929],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006975035,"about_ca_system_score_gemma":0.0007990717,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003857325,"about_ca_topic_score_gemma":0.005545823,"domain_scores_codex":[0.9994478,0.0001632359,0.00002179714,0.0001655101,0.0001339125,0.00006773147],"domain_scores_gemma":[0.9986104,0.000772296,0.0001373439,0.0002183652,0.0001944942,0.00006708215],"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":[0.0003724722,0.0002321705,0.002469202,0.0002597648,0.0001816163,0.0001900527,0.0002583332,0.34142,0.06590899,0.01402793,0.003772483,0.5709069],"study_design_scores_gemma":[0.00001201276,0.00004573881,0.000426597,0.00001073629,0.00001902618,0.00005712414,0.00001338881,0.9859951,0.006701259,0.006078717,0.0006318112,0.000008525855],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0211763,0.0005760491,0.9763018,0.0002086532,0.00003591784,0.00003824277,0.00004404672,0.0006635857,0.0009553934],"genre_scores_gemma":[0.614812,0.0006479229,0.3808767,0.0004962698,0.0001208463,0.0001033997,0.0002448664,0.0002052559,0.002492675],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003857325,"threshold_uncertainty_score":0.009848356,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02181851038999945,"score_gpt":0.2773361031655743,"score_spread":0.2555175927755748,"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."}}