{"id":"W2037954058","doi":"10.1109/cvpr.2011.5995344","title":"Global contrast based salient region detection","year":2011,"lang":"en","type":"article","venue":"","topic":"Visual Attention and Saliency Detection","field":"Computer Science","cited_by":3095,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"National Key Research and Development Program of China; National High-tech Research and Development Program; Engineering and Physical Sciences Research Council; National Natural Science Foundation of China","keywords":"Contrast (vision); Salient; Computer science; Artificial intelligence","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007674046,0.0009466065,0.001235742,0.003171806,0.0004114682,0.00101016,0.001361015,0.0007593778,0.002552141],"category_scores_gemma":[0.002730601,0.0004426175,0.0008480274,0.00119507,0.0004985542,0.001201429,0.001115827,0.0005554556,0.001005837],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005014302,"about_ca_system_score_gemma":0.000697679,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001514967,"about_ca_topic_score_gemma":0.002321317,"domain_scores_codex":[0.9994111,0.00005604481,0.00002703557,0.0001718272,0.0002616296,0.00007232976],"domain_scores_gemma":[0.999111,0.000229284,0.000141973,0.0001428985,0.0003209986,0.00005382787],"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":[0.0003679081,0.0001243629,0.002593582,0.0003546421,0.0001297059,0.0002757726,0.0001792926,0.02248124,0.26445,0.005534114,0.004611745,0.6988975],"study_design_scores_gemma":[0.00009390241,0.0004211339,0.01106959,0.00004036335,0.0001597401,0.001941423,0.0001380247,0.6926103,0.2709003,0.01225819,0.01027995,0.00008715774],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03530312,0.0005406268,0.960642,0.00005529934,0.00003337087,0.0001518298,0.000131053,0.001638526,0.00150419],"genre_scores_gemma":[0.3178796,0.0003768763,0.6782174,0.00008807841,0.00008106847,0.0001316019,0.0005814113,0.0003011647,0.002342749],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003171806,"threshold_uncertainty_score":0.008537769,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03919391967823426,"score_gpt":0.2500487382393543,"score_spread":0.21085481856112,"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."}}