{"id":"W4406391458","doi":"10.1145/3711929","title":"Wakeup-Darkness: When Multimodal Meets Unsupervised Low-Light Image Enhancement","year":2025,"lang":"en","type":"article","venue":"ACM Transactions on Multimedia Computing Communications and Applications","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"Canadian Mental Health Association; National Natural Science Foundation of China","keywords":"Computer science; Darkness; Artificial intelligence; Image (mathematics); Computer vision; Optics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004340711,0.0005187689,0.0003169979,0.0002690761,0.0003191403,0.0006328407,0.0006422008,0.0004756634,0.003924221],"category_scores_gemma":[0.001575623,0.0002173102,0.0002930206,0.0001593071,0.0006967657,0.00124376,0.001938486,0.0006476364,0.0007305089],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001967086,"about_ca_system_score_gemma":0.000272281,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005729366,"about_ca_topic_score_gemma":0.001183439,"domain_scores_codex":[0.9997777,0.00004727111,0.00000946503,0.00005873747,0.00006044523,0.00004644505],"domain_scores_gemma":[0.9995981,0.000133575,0.00003632841,0.0001148098,0.00006846367,0.00004867843],"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":[0.0009280472,0.0001865507,0.001973877,0.0002816088,0.00005039274,0.0004276413,0.000762525,0.03362866,0.529955,0.02166379,0.005403163,0.4047388],"study_design_scores_gemma":[0.00008677626,0.0006239894,0.005201573,0.0001033488,0.0000775826,0.0008293799,0.0003818418,0.5907507,0.3376883,0.03691098,0.02723886,0.0001066445],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1027211,0.0003806288,0.8862179,0.0002238369,0.00008567934,0.0001525652,0.0001088039,0.002332003,0.007777455],"genre_scores_gemma":[0.7031941,0.0002916846,0.2894026,0.0003005736,0.00005482108,0.0001532328,0.0001893994,0.0004912797,0.005922256],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003924221,"threshold_uncertainty_score":0.0131278,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01179820016179614,"score_gpt":0.2855765802176619,"score_spread":0.2737783800558657,"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."}}