{"id":"W2970617179","doi":"10.32470/ccn.2019.1079-0","title":"Gestalt-based Contour Weights Improve Scene Categorization by CNNs","year":2019,"lang":"en","type":"article","venue":"2019 Conference on Cognitive Computational Neuroscience","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto; McGill University","funders":"","keywords":"Categorization; Gestalt psychology; Computer science; Artificial intelligence; Pattern recognition (psychology); Computer vision; Psychology; Perception","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000228158,0.0003119892,0.0002582535,0.000216071,0.000239359,0.0004007475,0.0009487426,0.00008139434,0.00004196636],"category_scores_gemma":[0.000275482,0.0002923599,0.00007663703,0.0007218628,0.0002250017,0.001306917,0.0001772304,0.0002821108,0.0003241287],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006275878,"about_ca_system_score_gemma":0.0004517958,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001255396,"about_ca_topic_score_gemma":6.208245e-7,"domain_scores_codex":[0.9971375,0.0001373517,0.0003331446,0.001096596,0.0008414899,0.0004538682],"domain_scores_gemma":[0.9977844,0.0004952243,0.0002732973,0.0003478561,0.0009137585,0.0001854013],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002778738,0.001352347,0.003451881,0.0001117568,0.00002031118,0.00007980259,0.0003026622,0.003372809,0.2669561,0.5121137,0.003848325,0.2081125],"study_design_scores_gemma":[0.001523403,0.001594016,0.008348023,0.000185437,0.000008828112,0.0000116015,0.00002057178,0.8146034,0.1350043,0.03657532,0.001306236,0.0008187837],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01268785,0.00003473239,0.9822513,0.0008316335,0.0005979679,0.0006768566,0.0001024448,0.0003327651,0.002484433],"genre_scores_gemma":[0.9840372,0.00003237611,0.01057133,0.004249567,0.0000328372,0.00003015101,0.00007603253,0.00001871425,0.0009517842],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.97168,"threshold_uncertainty_score":0.9999529,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01981683765844714,"score_gpt":0.2861278843841405,"score_spread":0.2663110467256933,"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."}}