{"id":"W4404130826","doi":"10.1007/s00371-024-03700-z","title":"DMDC: a cross-attention network for dynamic mask-based dual-camera snapshot hyperspectral Photography","year":2024,"lang":"en","type":"article","venue":"The Visual Computer","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"Ministry of Education and Child Care","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Snapshot (computer storage); Hyperspectral imaging; Photography; Computer graphics (images); Computer science; Computer graphics; Artificial intelligence; Computational photography; Computer vision; Image processing; Art; Visual arts; Image (mathematics)","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":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0006829991,0.0002846909,0.0002158484,0.0001984581,0.0003006986,0.00113634,0.0008376801,0.00008140945,0.00003740267],"category_scores_gemma":[0.000004707059,0.0002145453,0.0003341904,0.0008344269,0.0001480815,0.0004711946,0.0002526489,0.0002525068,0.00004915624],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008080326,"about_ca_system_score_gemma":0.0000662442,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001207399,"about_ca_topic_score_gemma":0.000003368576,"domain_scores_codex":[0.9979722,0.0001073715,0.0003238579,0.0006525048,0.0003374994,0.0006066009],"domain_scores_gemma":[0.9989426,0.0002346489,0.00007614211,0.0005864142,0.00009503865,0.00006513712],"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":[0.0006289477,0.002000538,0.001996691,0.001441904,0.001664227,0.0004605295,0.005520566,0.01474204,0.1075953,0.2148633,0.2727377,0.3763482],"study_design_scores_gemma":[0.000327977,0.000542345,0.001158671,0.0001204238,0.00002621727,0.00001835617,0.000003429486,0.9822371,0.004004484,0.00530796,0.005919619,0.0003334383],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06587267,0.0003341139,0.9288835,0.00109449,0.001820355,0.0007516574,0.000007190512,0.001118117,0.000117891],"genre_scores_gemma":[0.7680609,0.00001058281,0.2293629,0.001457817,0.0006448546,0.000201459,0.00004961265,0.00003993683,0.0001719488],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.967495,"threshold_uncertainty_score":0.9999006,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01271756948333133,"score_gpt":0.3190374146363427,"score_spread":0.3063198451530114,"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."}}