{"id":"W3083923056","doi":"10.1109/tim.2020.3022438","title":"SEDRFuse: A Symmetric Encoder–Decoder With Residual Block Network for Infrared and Visible Image Fusion","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Instrumentation and Measurement","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":240,"is_retracted":false,"has_abstract":true,"ca_institutions":"Okanagan University College; University of British Columbia, Okanagan Campus","funders":"","keywords":"Computer science; Residual; Artificial intelligence; Block (permutation group theory); Image fusion; Computer vision; Compensation (psychology); Fuse (electrical); Encoder; Fusion; Feature extraction; Pixel; Feature (linguistics); Pattern recognition (psychology); Image (mathematics); Algorithm; Mathematics; 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":[0.0006937284,0.0009294137,0.0006113633,0.0004527732,0.0002905786,0.0004153134,0.001580228,0.0009098467,0.002299528],"category_scores_gemma":[0.000918345,0.0003079394,0.000514517,0.0003292905,0.0003609959,0.001155989,0.001166748,0.001095815,0.0008804833],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005094293,"about_ca_system_score_gemma":0.001033578,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004903423,"about_ca_topic_score_gemma":0.01065014,"domain_scores_codex":[0.9996874,0.00004237664,0.00001773965,0.0000634246,0.0001413502,0.00004772524],"domain_scores_gemma":[0.9997877,0.00004976351,0.00002473763,0.00003805086,0.00008036783,0.00001933141],"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":[0.000582681,0.0002998102,0.001416345,0.0001916852,0.0002192168,0.0002591609,0.0001034446,0.2014046,0.107808,0.009629809,0.008586446,0.6694987],"study_design_scores_gemma":[0.00002501396,0.0001298352,0.0003482919,0.00001135422,0.00003435053,0.0001247259,0.00001094646,0.9687772,0.02536882,0.002190303,0.002959482,0.00001967698],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01794107,0.000725429,0.9761674,0.0001328798,0.00009825969,0.00008169323,0.0001628335,0.002191078,0.002499278],"genre_scores_gemma":[0.4246,0.0006868459,0.5620227,0.0004145397,0.00009184156,0.0001866114,0.001258415,0.000216089,0.01052283],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004903423,"threshold_uncertainty_score":0.00974977,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02320607446375438,"score_gpt":0.2344929452837492,"score_spread":0.2112868708199948,"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."}}