{"id":"W2202819085","doi":"10.1371/journal.pone.0138053","title":"Visual Saliency Prediction and Evaluation across Different Perceptual Tasks","year":2015,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Visual Attention and Saliency Detection","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; University of Manitoba","keywords":"Computer science; Fixation (population genetics); Benchmarking; Artificial intelligence; Eye tracking; Perception; Visual perception; Gaze; Eye movement; Visual search; Pattern recognition (psychology); Psychophysics; Computational model; Machine learning; Computer vision; Population","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":[0.007411712,0.001608414,0.001024533,0.002333284,0.0004438518,0.001890824,0.001597544,0.001649534,0.001582998],"category_scores_gemma":[0.0463572,0.0003474352,0.000947077,0.001125018,0.000697548,0.002101668,0.001580897,0.0009428783,0.0005088649],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001184087,"about_ca_system_score_gemma":0.0007665897,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006725136,"about_ca_topic_score_gemma":0.004454988,"domain_scores_codex":[0.9957178,0.001687658,0.0003646587,0.0009459745,0.0009637104,0.0003202164],"domain_scores_gemma":[0.983376,0.010103,0.001048178,0.002142297,0.002794174,0.0005365197],"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":[0.00404868,0.001225527,0.05948382,0.001756967,0.001425046,0.0003554228,0.0007833122,0.459052,0.06231432,0.004981408,0.00618188,0.3983916],"study_design_scores_gemma":[0.00007783784,0.0008640553,0.02135433,0.0000515395,0.00009508168,0.0001645032,0.000120237,0.9487508,0.02395703,0.003697824,0.0008056401,0.00006116697],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6841545,0.001361309,0.3026895,0.0003543243,0.0002343319,0.0005310422,0.001235196,0.004204132,0.005235671],"genre_scores_gemma":[0.9359937,0.0001496876,0.06099043,0.00007530508,0.00003390859,0.0001560017,0.001699474,0.0003072457,0.0005941152],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007411712,"threshold_uncertainty_score":0.03919739,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1213671869892572,"score_gpt":0.3276533720032668,"score_spread":0.2062861850140096,"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."}}