{"id":"W2084226153","doi":"10.1109/icassp.2013.6637969","title":"3D motion in visual saliency modeling","year":2013,"lang":"en","type":"article","venue":"","topic":"Visual Attention and Saliency Detection","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Artificial intelligence; Computer vision; Computer science; Kadir–Brady saliency detector; Pixel; Luminance; Flicker; Human visual system model; Observer (physics); Feature (linguistics); Motion estimation; Motion (physics); Contrast (vision); Pattern recognition (psychology); Saliency map; Image (mathematics); Computer graphics (images)","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0001389324,0.00006516667,0.00006318687,0.0001445799,0.00004884302,0.0001148412,0.0001944385,0.00003872386,0.0001558111],"category_scores_gemma":[0.00001242354,0.00005683252,0.00002779584,0.0004068786,0.000006392302,0.0009537789,0.00006636527,0.00006967971,0.0006393395],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003053705,"about_ca_system_score_gemma":0.000009454207,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003541248,"about_ca_topic_score_gemma":0.00003440468,"domain_scores_codex":[0.9992283,0.00003687808,0.0001839685,0.0002234804,0.0001597667,0.0001675941],"domain_scores_gemma":[0.9997376,0.000006779127,0.00002146991,0.0001387564,0.00004970293,0.00004566669],"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":[0.000003458306,0.0005850726,0.006536348,0.00002136124,0.000006713559,0.000005313095,0.001275832,0.02512259,0.01739988,0.08783373,0.0003454503,0.8608643],"study_design_scores_gemma":[0.000125803,0.00003797603,0.003701664,0.00000414705,3.332173e-7,0.0000029467,0.00004552248,0.9920576,0.0003534419,0.003570355,0.00002096018,0.0000792162],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3253921,0.000004968565,0.6700773,0.0002209989,0.0001757165,0.00008025877,1.86329e-8,0.0001279117,0.003920761],"genre_scores_gemma":[0.9910796,0.000003362047,0.00825339,0.0002249856,0.00002079417,0.00001921604,5.621727e-7,0.000003228436,0.0003948748],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.966935,"threshold_uncertainty_score":0.8217629,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01731745405141681,"score_gpt":0.2681261580622799,"score_spread":0.2508087040108631,"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."}}