{"id":"W3081366416","doi":"10.3390/electronics9091356","title":"FCC-Net: A Full-Coverage Collaborative Network for Weakly Supervised Remote Sensing Object Detection","year":2020,"lang":"en","type":"article","venue":"Electronics","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"National Natural Science Foundation of China","keywords":"Computer science; Robustness (evolution); Artificial intelligence; Pooling; Detector; Object detection; Benchmark (surveying); Object (grammar); Computer vision; Ground truth; Pattern recognition (psychology); Cognitive neuroscience of visual object recognition; Feature extraction","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":[0.0001427482,0.0002140553,0.0002280227,0.00003166794,0.0003968144,0.000125975,0.0004293439,0.00008712773,0.000001796649],"category_scores_gemma":[0.00007843135,0.0002324719,0.00008814969,0.001581426,0.00002515175,0.0003253761,0.0001113933,0.0002989293,0.00002222418],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001687301,"about_ca_system_score_gemma":0.0002019653,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001860188,"about_ca_topic_score_gemma":0.0001359822,"domain_scores_codex":[0.9982031,0.00008353261,0.0002593349,0.0006040799,0.0001918534,0.0006580908],"domain_scores_gemma":[0.9988818,0.000245911,0.0001421268,0.000421937,0.000173019,0.0001351759],"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":[0.0002032976,0.00001753624,0.000001793774,0.00002113923,0.00005993443,0.000006602315,0.0007595799,0.05385507,0.09843537,0.009780877,0.002461509,0.8343973],"study_design_scores_gemma":[0.0004757167,0.0005473625,0.000008841844,0.000009237732,0.00001488,0.00001476651,0.00001201017,0.8744178,0.02189562,0.02417596,0.07814487,0.0002829155],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00639027,0.001681365,0.9872779,0.003051844,0.0001680115,0.0008467173,0.000005624541,0.0004018379,0.0001764537],"genre_scores_gemma":[0.4997732,0.001030007,0.4938501,0.003822455,0.001285123,0.00003111805,0.00002915228,0.0000800202,0.00009874851],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8341144,"threshold_uncertainty_score":0.9479927,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01317179961630162,"score_gpt":0.2465786810729113,"score_spread":0.2334068814566097,"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."}}