{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005688813,0.0008851768,0.0004483173,0.000748663,0.00026642,0.0008349638,0.0007368255,0.0006506993,0.002997954],"category_scores_gemma":[0.001430306,0.0003755437,0.0009160143,0.0007718278,0.0003276459,0.0009691696,0.000911504,0.0008587222,0.001226298],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003041971,"about_ca_system_score_gemma":0.0004871626,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002868416,"about_ca_topic_score_gemma":0.005096331,"domain_scores_codex":[0.9998189,0.00003122056,0.00001135913,0.00005131011,0.00005705907,0.00003018582],"domain_scores_gemma":[0.9996533,0.00008336881,0.00003326022,0.0001291043,0.00008029352,0.0000205917],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003982314,0.0001745171,0.002686239,0.0003366518,0.0001856856,0.0004207997,0.0001829911,0.1086126,0.3484026,0.005996659,0.003028561,0.5295745],"study_design_scores_gemma":[0.00002073085,0.0001467588,0.01199705,0.00003550422,0.0001425485,0.001115946,0.00009658974,0.7778382,0.1877758,0.009522767,0.01126669,0.000041557],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02747969,0.0002671171,0.9690632,0.0001420384,0.00004822383,0.00006316743,0.0001784485,0.00106212,0.001695942],"genre_scores_gemma":[0.2927925,0.001627305,0.696199,0.0002746575,0.000172057,0.0001041902,0.001052949,0.0007374749,0.007039905],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002997954,"threshold_uncertainty_score":0.0100292,"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."}}