{"id":"W2803486575","doi":"10.29007/xhlz","title":"Significance of Bottom-up Attributes in Video Saliency Detection Without Cognitive Bias","year":2018,"lang":"en","type":"paratext","venue":"EasyChair preprint","topic":"Visual Attention and Saliency Detection","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Optimal distinctiveness theory; Salient; Computer science; Artificial intelligence; Human visual system model; Perception; Cognition; Computer vision; Visual search; Eye tracking; Ranking (information retrieval); Pattern recognition (psychology); Cognitive psychology; Psychology; 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":[0.003657522,0.0005982241,0.0006752933,0.0008362856,0.0005185921,0.001353498,0.0005436767,0.0005105793,0.001329604],"category_scores_gemma":[0.03453793,0.0002940056,0.000545588,0.0004758697,0.0008033412,0.00196656,0.001148466,0.0006695304,0.0001975341],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007098351,"about_ca_system_score_gemma":0.0006523317,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003037055,"about_ca_topic_score_gemma":0.002034927,"domain_scores_codex":[0.9975897,0.0009710336,0.000115542,0.0004535287,0.0006235053,0.0002466509],"domain_scores_gemma":[0.986576,0.009428415,0.0007376174,0.001232161,0.001574094,0.0004516724],"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":[0.004933708,0.0007644857,0.05129144,0.001095873,0.0003246221,0.0003950216,0.001611669,0.06392542,0.2634476,0.02481517,0.001316883,0.5860782],"study_design_scores_gemma":[0.0001582073,0.002559209,0.1132755,0.00009927797,0.0003061589,0.000403424,0.000531332,0.7491895,0.08882584,0.04248158,0.001986408,0.0001835305],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6617181,0.0008107202,0.3289436,0.0003102458,0.00007768501,0.0002929785,0.0001559101,0.0003800769,0.007310711],"genre_scores_gemma":[0.970466,0.00007424537,0.02907057,0.00002604158,0.00003191442,0.00004282629,0.00005572421,0.00002415473,0.0002084281],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003657522,"threshold_uncertainty_score":0.01934302,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04829990113790905,"score_gpt":0.3169496395203378,"score_spread":0.2686497383824288,"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."}}