{"id":"W4372259885","doi":"10.1109/icassp49357.2023.10095364","title":"ifUNet++: Iterative Feedback UNet++ for Infrared Small Target Detection","year":2023,"lang":"en","type":"article","venue":"","topic":"Infrared Target Detection Methodologies","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Novelis (Canada)","funders":"National Natural Science Foundation of China","keywords":"Clutter; Robustness (evolution); Computer science; Iterative method; Infrared; Interference (communication); Noise (video); Artificial intelligence; Algorithm; Computer vision; Radar; Telecommunications; Image (mathematics); Optics; Physics","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":[0.0007920005,0.001749263,0.001269861,0.001038853,0.0007276918,0.0009379566,0.003405506,0.001150955,0.003554952],"category_scores_gemma":[0.002882292,0.0005411684,0.0008370086,0.0006072564,0.0006053172,0.00199668,0.00218412,0.001267482,0.00142142],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006237205,"about_ca_system_score_gemma":0.0009601379,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006501568,"about_ca_topic_score_gemma":0.008933445,"domain_scores_codex":[0.9993194,0.00009982619,0.00003049403,0.0002178648,0.000222265,0.0001100974],"domain_scores_gemma":[0.9989938,0.0003768767,0.00007859601,0.0001947534,0.0002803992,0.00007553193],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0005099342,0.000212676,0.002150933,0.0002648206,0.0001127715,0.0003146651,0.0002051739,0.1089664,0.04510105,0.004014992,0.01171374,0.8264329],"study_design_scores_gemma":[0.00003009195,0.0001499455,0.000469347,0.00001389235,0.00003476342,0.0002431345,0.00003297461,0.9776236,0.01487832,0.002637469,0.003863754,0.00002275397],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01576989,0.0004858459,0.9736224,0.0001135593,0.0001108055,0.0001131572,0.0001124681,0.00704913,0.002622763],"genre_scores_gemma":[0.3410647,0.0003850181,0.6485326,0.0005888942,0.0001214143,0.0002675239,0.00086895,0.0008019255,0.007369006],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006501568,"threshold_uncertainty_score":0.01292741,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04686385811389072,"score_gpt":0.2631381456333048,"score_spread":0.2162742875194141,"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."}}