{"id":"W4402571640","doi":"10.1109/iceict61637.2024.10671123","title":"A Salient Feature-guided Network Using a Two-stage Transfer Learning Strategy for Few-Shot Aircraft Detection in SAR Images","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced SAR Imaging Techniques","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China; Ministry of Natural Resources","keywords":"Salient; Shot (pellet); Computer science; Artificial intelligence; Feature (linguistics); Transfer of learning; Stage (stratigraphy); One shot; Feature extraction; Single shot; Synthetic aperture radar; Computer vision; Pattern recognition (psychology); Engineering; Geology; Materials science; Physics; Optics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002715879,0.00024464,0.0002195431,0.0001884645,0.00007208433,0.0001268382,0.00009641726,0.00009733128,0.00002535924],"category_scores_gemma":[0.00001853974,0.0002498973,0.0001019559,0.0004078782,0.00002431889,0.0003286287,0.00001376077,0.0004682789,0.000004006425],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002528379,"about_ca_system_score_gemma":0.0000217544,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007384089,"about_ca_topic_score_gemma":0.0001560348,"domain_scores_codex":[0.9988208,0.00003413634,0.0002532706,0.000314561,0.0001173634,0.0004598889],"domain_scores_gemma":[0.9996732,0.000085161,0.000009069838,0.0001527533,0.00002913054,0.00005071632],"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":[0.00001224776,0.00000598042,0.00005282987,0.0001752884,0.00002329389,0.00002234541,0.0001277385,0.8774006,0.1101682,0.0004063791,0.0005301915,0.01107483],"study_design_scores_gemma":[0.0002861242,0.00004911275,0.00003817906,0.0001975359,0.00002273903,0.0000187727,0.00009574307,0.8478611,0.1417522,0.001369855,0.007971837,0.0003368518],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05128228,0.000979839,0.9425161,0.0000489545,0.0002646515,0.0005116144,0.000007284295,0.002859908,0.001529315],"genre_scores_gemma":[0.9213289,0.00003994163,0.07778089,0.00003398525,0.0001721912,0.00005196934,0.00000957072,0.0001179121,0.0004646698],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8700466,"threshold_uncertainty_score":0.9999954,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03518169436219995,"score_gpt":0.3185292771065588,"score_spread":0.2833475827443589,"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."}}