{"id":"W2074678338","doi":"10.5244/c.24.110","title":"Saliency Segmentation based on Learning and Graph Cut Refinement","year":2010,"lang":"en","type":"article","venue":"","topic":"Visual Attention and Saliency Detection","field":"Computer Science","cited_by":58,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Computer science; Artificial intelligence; Segmentation; Image segmentation; Graph; Computer vision; Pattern recognition (psychology); Theoretical computer science","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":[0.001687335,0.001590376,0.001929164,0.00353581,0.0006486548,0.001274346,0.003122004,0.002156889,0.002015502],"category_scores_gemma":[0.00779769,0.0009336869,0.001395914,0.001895331,0.001268526,0.002371973,0.001412771,0.001710758,0.0008417684],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001743323,"about_ca_system_score_gemma":0.001163232,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007261491,"about_ca_topic_score_gemma":0.00931729,"domain_scores_codex":[0.9985986,0.0002303261,0.00007311394,0.0004730833,0.0004838562,0.0001409081],"domain_scores_gemma":[0.9963266,0.001581227,0.0004484206,0.0005455627,0.0009514783,0.0001466951],"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":[0.0002938455,0.0002066693,0.002660842,0.0002872846,0.000146324,0.0002002692,0.0003074628,0.4078627,0.04096597,0.01355995,0.005999229,0.5275095],"study_design_scores_gemma":[0.00001519024,0.00004666621,0.0004522891,0.000008525767,0.00001301672,0.00006587119,0.00001547039,0.9842779,0.006594583,0.007839919,0.0006599522,0.00001058042],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0172438,0.0001891465,0.9796458,0.0001056467,0.00002309151,0.0001252312,0.00006585138,0.001787934,0.0008134361],"genre_scores_gemma":[0.2494663,0.0001714204,0.7469873,0.0001760795,0.00007178419,0.0002117205,0.0006233641,0.0005341459,0.001757934],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007261491,"threshold_uncertainty_score":0.01443845,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009763177913905144,"score_gpt":0.274348624450405,"score_spread":0.2645854465364999,"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."}}