{"id":"W4403779027","doi":"10.1007/978-3-031-72698-9_22","title":"Learning Exhaustive Correlation for Spectral Super-Resolution: Where Spatial-Spectral Attention Meets Linear Dependence","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Huawei Technologies (Canada)","funders":"National Natural Science Foundation of China","keywords":"Computer science; Correlation; Spatial correlation; Algorithm; Linear correlation; Artificial intelligence; Statistical physics; Pattern recognition (psychology); Physics; Statistics; Mathematics; Telecommunications; Geometry","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":[0.001642469,0.001164779,0.001718587,0.0006220503,0.0004796387,0.001486681,0.001847474,0.001941715,0.004436426],"category_scores_gemma":[0.006402484,0.0008264887,0.0007343325,0.001310388,0.001092278,0.003099751,0.002701828,0.002410529,0.001110121],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006433918,"about_ca_system_score_gemma":0.001890486,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004272697,"about_ca_topic_score_gemma":0.007974098,"domain_scores_codex":[0.9994414,0.0001438058,0.00003052615,0.0001639785,0.0001218652,0.00009837164],"domain_scores_gemma":[0.9978781,0.001435471,0.00008873828,0.0002963991,0.0002088804,0.00009242637],"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":[0.0005201612,0.0003119165,0.0009336406,0.0004185496,0.0002086298,0.0002663631,0.0002151772,0.2474681,0.03289604,0.07973395,0.01557649,0.6214509],"study_design_scores_gemma":[0.000008548071,0.00004391586,0.0001731282,0.00001413218,0.00002098115,0.00007954321,0.00001450782,0.9693078,0.003985734,0.02522733,0.001113933,0.00001049563],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01291009,0.0008738004,0.9827801,0.0004175315,0.00006634163,0.00002812346,0.0001014776,0.0005301783,0.002292313],"genre_scores_gemma":[0.4639039,0.002043935,0.5166405,0.0009252276,0.0005004703,0.0001624202,0.0006352968,0.0004564975,0.01473173],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004436426,"threshold_uncertainty_score":0.01484138,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01011856243330203,"score_gpt":0.2437053857906674,"score_spread":0.2335868233573654,"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."}}