{"id":"W2941877317","doi":"10.1109/mipr.2019.00041","title":"Saliency Priority Using Bottom-up Features for Static and Dynamic Scenes Without Cognitive Bias","year":2019,"lang":"en","type":"article","venue":"2019 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR)","topic":"Visual Attention and Saliency Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Artificial intelligence; Salient; Computer vision; Human visual system model; Eye tracking; Video tracking; Robustness (evolution); Pattern recognition (psychology); Object (grammar); 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001240545,0.000718838,0.0007363124,0.001473927,0.0003859767,0.001050423,0.0007124307,0.0005471248,0.001767557],"category_scores_gemma":[0.008838336,0.0002830431,0.0006568663,0.0005256187,0.0005348297,0.001677692,0.0008480428,0.0006015549,0.0002298531],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009259573,"about_ca_system_score_gemma":0.0006965549,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004042199,"about_ca_topic_score_gemma":0.003980197,"domain_scores_codex":[0.9992682,0.0001382359,0.00004010101,0.0002050162,0.0002244077,0.0001239376],"domain_scores_gemma":[0.9977553,0.001067231,0.000273925,0.0002473066,0.000509875,0.00014631],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00249268,0.0006614889,0.01936181,0.0007902657,0.0002393707,0.0004765623,0.0007228041,0.10686,0.2933511,0.01837315,0.002641357,0.5540294],"study_design_scores_gemma":[0.00008708538,0.0009221461,0.03288498,0.00004492829,0.0001354753,0.00033247,0.0001638723,0.9033678,0.04501518,0.01525645,0.001685997,0.0001035491],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.316503,0.0004166265,0.6779153,0.0001450732,0.00005997771,0.0002988351,0.0002159853,0.0009082545,0.00353685],"genre_scores_gemma":[0.9193497,0.00007866853,0.07978228,0.00003051086,0.0000327487,0.00005685211,0.0001404736,0.00004655892,0.0004821937],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004042199,"threshold_uncertainty_score":0.008037388,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03595719900652511,"score_gpt":0.321767415896429,"score_spread":0.2858102168899039,"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."}}