{"id":"W3115729019","doi":"10.1145/3426239","title":"Deep Learning Thermal Image Translation for Night Vision Perception","year":2020,"lang":"en","type":"article","venue":"ACM Transactions on Intelligent Systems and Technology","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":45,"is_retracted":false,"has_abstract":true,"ca_institutions":"Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Image translation; Computer science; Artificial intelligence; Convolutional neural network; Translation (biology); Deep learning; Computer vision; Perception; Grayscale; Image (mathematics); Pattern recognition (psychology)","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":[0.000253357,0.0006895488,0.0004078378,0.0002497994,0.0002051428,0.0005291521,0.0007619406,0.0004940293,0.001854949],"category_scores_gemma":[0.0007222184,0.0001945011,0.0005967399,0.000300382,0.0003647234,0.0009657383,0.0007680953,0.001301423,0.0004663686],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004747183,"about_ca_system_score_gemma":0.000484311,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002420663,"about_ca_topic_score_gemma":0.003153876,"domain_scores_codex":[0.9998424,0.00002504996,0.000004194253,0.00005207633,0.00004622569,0.00003004539],"domain_scores_gemma":[0.9998761,0.00002784834,0.00001808425,0.0000318309,0.00003395239,0.00001208723],"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":[0.0003227711,0.000166979,0.001324314,0.0002033322,0.0001140283,0.0002072522,0.0001413037,0.2586346,0.1336658,0.01159061,0.006050744,0.5875782],"study_design_scores_gemma":[0.000008537116,0.00006705695,0.0007569639,0.00001094946,0.00002823331,0.00007508697,0.00002206496,0.9698379,0.02139034,0.004784193,0.003005863,0.00001282243],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05747888,0.001038377,0.9341554,0.0002851022,0.0001956799,0.00004674241,0.0001256126,0.001879941,0.004794328],"genre_scores_gemma":[0.7892332,0.001038904,0.2003613,0.0004270966,0.0001512592,0.00005868769,0.0004907571,0.0003187648,0.007919949],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002420663,"threshold_uncertainty_score":0.00620544,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02176842157346652,"score_gpt":0.2753056018862906,"score_spread":0.253537180312824,"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."}}