{"id":"W1963666854","doi":"10.1109/cimsa.2011.6059933","title":"Salient features based on visual attention for multi-view vehicle classification","year":2011,"lang":"en","type":"article","venue":"","topic":"Visual Attention and Saliency Detection","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Salient; Discriminative model; Computer science; Set (abstract data type); Support vector machine; Artificial intelligence; Binary classification; Contextual image classification; Binary number; Pattern recognition (psychology); Machine learning; Data mining; Image (mathematics)","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":[0.0003046924,0.0001175829,0.0000966932,0.0001304086,0.0001712367,0.00007679319,0.0002616034,0.00006992253,0.00004248105],"category_scores_gemma":[0.00003018265,0.00009779941,0.0001184807,0.0002468862,0.000020523,0.0002710678,0.00002793254,0.00006905211,0.0001328504],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004980412,"about_ca_system_score_gemma":0.00002320286,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002089968,"about_ca_topic_score_gemma":0.00002006521,"domain_scores_codex":[0.9989125,0.00006823851,0.0002065518,0.0003865556,0.0002140978,0.0002121306],"domain_scores_gemma":[0.9994078,0.00002445051,0.00008807715,0.0002818478,0.0001168512,0.00008099272],"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":[0.0002613215,0.004406803,0.00553299,0.0001540058,0.00004449861,0.000003854774,0.0008015918,0.0001012528,0.06887539,0.4117652,0.005082007,0.5029711],"study_design_scores_gemma":[0.0007869311,0.0005535474,0.1921267,0.00002071609,0.000007348855,0.00000101029,0.00004663645,0.7947395,0.01025696,0.0004510684,0.0008355245,0.0001740683],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02069224,0.000009412911,0.9756054,0.0004307806,0.000562272,0.0004021068,0.00000129257,0.0003106092,0.001985919],"genre_scores_gemma":[0.9580887,0.000002620635,0.03965109,0.001019875,0.00003254151,0.0001009572,0.000009765717,0.000009284673,0.00108516],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9373965,"threshold_uncertainty_score":0.3988144,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1112003839541393,"score_gpt":0.3398677068014967,"score_spread":0.2286673228473574,"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."}}