{"id":"W3028020181","doi":"10.1155/2020/7286187","title":"Learning Feature Fusion in Deep Learning-Based Object Detector","year":2020,"lang":"en","type":"article","venue":"Journal of Engineering","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Artificial intelligence; Computer science; Pascal (unit); Object detection; Deep learning; Convolutional neural network; Pattern recognition (psychology); Feature extraction; Feature (linguistics); Detector; Feature learning; Machine learning; Computer vision","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":[0.0001243663,0.00009513679,0.0001520517,0.0001151707,0.00004108532,0.00003815795,0.0003540862,0.00004266313,0.000003280947],"category_scores_gemma":[0.0002641684,0.00008970026,0.00006444209,0.0006087324,0.000004406108,0.0002469366,0.00006000352,0.0008175858,0.000004375172],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004958937,"about_ca_system_score_gemma":0.00002208452,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":3.899915e-7,"about_ca_topic_score_gemma":8.066804e-7,"domain_scores_codex":[0.9992998,0.00002721553,0.0002060473,0.0001231021,0.0001758771,0.0001679333],"domain_scores_gemma":[0.9994648,0.0001414255,0.000147931,0.00008266251,0.00005123775,0.0001119664],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000005623531,0.000005010601,0.0004649179,0.000009138063,0.000002703407,0.00003442468,0.0001705099,0.9693419,0.02152969,0.00003965637,0.00001447994,0.008381927],"study_design_scores_gemma":[0.0002827613,0.0001684877,0.002381956,0.00004569177,0.000002549014,0.00002504196,0.00001153729,0.9870635,0.003661753,0.00001199343,0.006247154,0.00009759152],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0807844,0.0003510938,0.9173697,0.001248222,0.00009938013,0.00004872018,4.609689e-8,0.00007956826,0.00001884251],"genre_scores_gemma":[0.9342583,0.00002351103,0.06542519,0.0001015331,0.0001681069,0.000001905352,2.209184e-7,0.00001225889,0.000008999386],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8534738,"threshold_uncertainty_score":0.365787,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008148556892061912,"score_gpt":0.2164947666093686,"score_spread":0.2083462097173067,"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."}}