{"id":"W4392908914","doi":"10.1109/icassp48485.2024.10447233","title":"Boosting Image Quality Assessment Performance: Unsupervised Score Fusion by Deep Maximum a Posteriori Estimation","year":2024,"lang":"en","type":"article","venue":"","topic":"Image and Video Quality Assessment","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University; University of Waterloo","funders":"","keywords":"Computer science; Boosting (machine learning); Artificial intelligence; A priori and a posteriori; Machine learning; Maximum a posteriori estimation; Strengths and weaknesses; Image quality; Process (computing); Quality (philosophy); Image fusion; Pattern recognition (psychology); Image (mathematics); Fusion; Quality Score; Data mining; Statistics; Maximum likelihood; Mathematics; Engineering; Metric (unit)","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":[0.003272326,0.001355473,0.001286926,0.001257368,0.0003102065,0.001155443,0.001415874,0.001132679,0.0009281274],"category_scores_gemma":[0.008375403,0.000504515,0.001009401,0.000686969,0.0008244365,0.00198501,0.002281098,0.00173671,0.0003824933],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005833096,"about_ca_system_score_gemma":0.0008279415,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002840733,"about_ca_topic_score_gemma":0.00269836,"domain_scores_codex":[0.9986547,0.0004023726,0.00007466093,0.000257892,0.0004971129,0.0001132557],"domain_scores_gemma":[0.9975742,0.001013967,0.0002990923,0.0003102825,0.0006809046,0.0001215583],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004611801,0.0002234865,0.004384916,0.0001544455,0.0003559766,0.000107558,0.0001781361,0.5007293,0.02913916,0.0080411,0.002574614,0.4536501],"study_design_scores_gemma":[0.000009328158,0.00004997069,0.0006350435,0.000007617467,0.00002289545,0.00002816648,0.000007570191,0.9918602,0.00379796,0.003298418,0.0002699193,0.00001299128],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02325848,0.0003398468,0.9747925,0.0001561268,0.00003055746,0.00003080136,0.00003451695,0.0005405525,0.000816613],"genre_scores_gemma":[0.7382527,0.0003976685,0.2584499,0.0002502806,0.0001286739,0.00008164551,0.0002830141,0.0001895462,0.001966603],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003272326,"threshold_uncertainty_score":0.01730591,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03613297461971213,"score_gpt":0.3434529637545939,"score_spread":0.3073199891348818,"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."}}