{"id":"W4400390978","doi":"10.1016/j.neucom.2024.128132","title":"Generalizing event-based HDR imaging to various exposures","year":2024,"lang":"en","type":"article","venue":"Neurocomputing","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"Natural Science Foundation of Hubei Province; National Natural Science Foundation of China","keywords":"Computer science; Event (particle physics); Artificial intelligence; Computer vision; Pattern recognition (psychology); 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004050668,0.0002109676,0.0001483884,0.0003105344,0.0001884123,0.0008817393,0.0008751085,0.00002659167,0.00000771705],"category_scores_gemma":[0.00004251646,0.0002173897,0.00008971975,0.0007082244,0.00001475542,0.0004031901,0.0005016654,0.0002207752,0.0001011363],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007092027,"about_ca_system_score_gemma":0.00008293277,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002663448,"about_ca_topic_score_gemma":0.000001543444,"domain_scores_codex":[0.9980486,0.00008478322,0.0003110842,0.000723125,0.000344134,0.0004882669],"domain_scores_gemma":[0.9991698,0.0001198118,0.00004607682,0.0005062155,0.00005488465,0.0001031815],"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":[0.000002850884,0.00005109394,0.0005456964,0.0001103768,0.00001347284,0.0007681167,0.001029137,0.009760403,0.1908003,0.01090712,0.01350517,0.7725063],"study_design_scores_gemma":[0.00008582292,0.0000565294,0.0003099327,0.000187312,0.000004622494,0.0000366348,0.000004156226,0.84813,0.1248106,0.0004202143,0.02567636,0.0002777952],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01887227,0.0002722767,0.9738794,0.001906881,0.0009916143,0.0002676458,4.89936e-7,0.002834446,0.0009749695],"genre_scores_gemma":[0.7204712,9.80498e-7,0.2759939,0.003196338,0.0002248077,0.00002105534,9.133092e-7,0.00002792726,0.00006294323],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8383697,"threshold_uncertainty_score":0.8864895,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01110697860243358,"score_gpt":0.2772299438446134,"score_spread":0.2661229652421798,"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."}}