{"id":"W3101501534","doi":"","title":"An HVS-Oriented Saliency Map Prediction Modeling.","year":2020,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Visual Attention and Saliency Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Salience (neuroscience); Artificial intelligence; Human visual system model; Computer science; Redundancy (engineering); Bottleneck; Pattern recognition (psychology); Visual cortex; Visual memory; Visualization; Wavelet; Computer vision; Image (mathematics); Cognition; Psychology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002590298,0.000508111,0.0003163199,0.0006396771,0.0001620514,0.0004290043,0.0008585455,0.0004132056,0.001719111],"category_scores_gemma":[0.000746821,0.0002037574,0.0006044428,0.0003953181,0.0001932224,0.000610631,0.0003366559,0.000437623,0.0003516504],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000641436,"about_ca_system_score_gemma":0.0004224569,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008909775,"about_ca_topic_score_gemma":0.007171186,"domain_scores_codex":[0.9999167,0.00001207994,0.000003082701,0.0000303837,0.00002365529,0.00001421141],"domain_scores_gemma":[0.9998719,0.00003633285,0.00001738768,0.00001093589,0.00005166451,0.00001182046],"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":[0.0002550121,0.000148502,0.005181256,0.0001843483,0.0001563005,0.0002052201,0.0001432003,0.6530792,0.02493837,0.01506042,0.009505806,0.2911424],"study_design_scores_gemma":[0.000002286376,0.00001315911,0.0004975275,0.000002579426,0.000007521042,0.00001718007,0.000003359941,0.9970823,0.0006712759,0.001375751,0.0003244078,0.000002675069],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07205373,0.001557838,0.9175188,0.0004895334,0.0002107792,0.0001024166,0.0005424119,0.001239704,0.006284883],"genre_scores_gemma":[0.9294526,0.00046803,0.06475212,0.00009885443,0.000100011,0.00006383508,0.0003980522,0.00006257711,0.004603983],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008909775,"threshold_uncertainty_score":0.01771581,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07464700690625743,"score_gpt":0.1934957286786649,"score_spread":0.1188487217724074,"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."}}