{"id":"W2301300958","doi":"10.1109/tmm.2016.2522639","title":"Human Visual System-Based Saliency Detection for High Dynamic Range Content","year":2016,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Visual Attention and Saliency Detection","field":"Computer Science","cited_by":44,"is_retracted":false,"has_abstract":true,"ca_institutions":"Telus (Canada); University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Human visual system model; Artificial intelligence; Computer vision; Salient; High dynamic range; Computer graphics; Visualization; Graphics; Range (aeronautics); Computational model; Human eye; Dynamic range; Computer graphics (images); Image (mathematics)","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":[0.0002908666,0.0002451758,0.0002337666,0.0003688489,0.0004950242,0.00007759559,0.0003387758,0.0001528577,0.00003482464],"category_scores_gemma":[0.00001118803,0.0001862421,0.0002386398,0.000324351,0.00007234878,0.0004240941,0.000001825301,0.0001306219,0.0001928118],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003410621,"about_ca_system_score_gemma":0.0000407344,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008911557,"about_ca_topic_score_gemma":0.0002204781,"domain_scores_codex":[0.9981241,0.0001235543,0.0004261763,0.0005814626,0.0003724968,0.000372264],"domain_scores_gemma":[0.9989393,0.0001686864,0.0001435049,0.0003963608,0.0001926115,0.0001595849],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001288047,0.0006129238,0.00001571211,0.00008458129,0.00005344765,0.00000500955,0.0001388973,0.0005084093,0.5309628,0.0003527538,0.00002683749,0.4671098],"study_design_scores_gemma":[0.007265876,0.002069292,0.003061933,0.0002211151,0.00007551551,0.00001862819,0.00009953399,0.4998718,0.4863548,0.0001182408,0.0001671305,0.0006761634],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09696929,0.000006081769,0.8983595,0.0002824377,0.002955108,0.000666944,0.00003454971,0.000703313,0.00002277868],"genre_scores_gemma":[0.9943731,0.000002373941,0.004358373,0.00009676925,0.00006114929,0.0004437947,0.000002382764,0.00002834296,0.0006337297],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8974038,"threshold_uncertainty_score":0.7594733,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02675596534092534,"score_gpt":0.2841701884584154,"score_spread":0.2574142231174901,"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."}}