{"id":"W4377236361","doi":"10.1145/3581807.3581815","title":"Coordinate Attention-enabled Ship Object Detection with Electro-optical Image","year":2022,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"National Natural Science Foundation of China","keywords":"Computer science; Object detection; Object (grammar); Computer vision; Coordinate system; Artificial intelligence; Set (abstract data type); Range (aeronautics); Image (mathematics); Shore; Attention network; Real-time computing; Simulation; Pattern recognition (psychology); Engineering; Aerospace engineering","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":[0.0004168639,0.0005629692,0.0004237335,0.0007386886,0.0002874742,0.0005503286,0.0009239629,0.0005210002,0.0009088165],"category_scores_gemma":[0.001129223,0.0002608747,0.0004194066,0.0005452291,0.0003384433,0.0009498522,0.0006756689,0.0004223595,0.0003013044],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008119927,"about_ca_system_score_gemma":0.000499417,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01061011,"about_ca_topic_score_gemma":0.009983216,"domain_scores_codex":[0.9997327,0.00002801703,0.000008556414,0.0001002081,0.00007709569,0.00005344134],"domain_scores_gemma":[0.9996929,0.00007700327,0.00003767194,0.00003493857,0.0001382642,0.00001922391],"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":[0.0005225962,0.0003007917,0.00836801,0.00009898043,0.0001191239,0.0002556743,0.0001764162,0.1326562,0.09867556,0.001898068,0.003306366,0.7536221],"study_design_scores_gemma":[0.00001020179,0.0001034493,0.005249852,0.000005518682,0.00003283217,0.00008988799,0.00002577336,0.964835,0.02841605,0.0004810514,0.0007390893,0.00001139865],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2963288,0.0007031614,0.694389,0.0002683006,0.0001707543,0.00008615386,0.0001331513,0.002865513,0.005055014],"genre_scores_gemma":[0.9292572,0.0001563339,0.0678767,0.0001031372,0.00003851822,0.00002724166,0.0001262275,0.00003212701,0.002382609],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01061011,"threshold_uncertainty_score":0.02109671,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008073177542105027,"score_gpt":0.2292921173488088,"score_spread":0.2212189398067038,"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."}}