{"id":"W3004871661","doi":"10.1109/pacrim47961.2019.8985104","title":"Video Super-Resolution with Compensation in Feature Extraction","year":2019,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Artificial intelligence; Motion compensation; Pixel; Computer vision; Compensation (psychology); Optical flow; Residual; Feature extraction; Convolutional neural network; Feature (linguistics); Frame (networking); Bilateral filter; Motion estimation; Pattern recognition (psychology); Filter (signal processing); Image (mathematics); Algorithm","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":[0.0003486628,0.0005134103,0.0004193626,0.0005586112,0.0001392397,0.0002919173,0.0005388876,0.0004279393,0.001447287],"category_scores_gemma":[0.0006813893,0.0002403347,0.0004100779,0.0006278259,0.0002295128,0.0009056489,0.0005095243,0.0005627606,0.0004561426],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003021216,"about_ca_system_score_gemma":0.0003289372,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00166565,"about_ca_topic_score_gemma":0.002930537,"domain_scores_codex":[0.9997111,0.0000339487,0.00001472431,0.00006361521,0.000147532,0.00002903266],"domain_scores_gemma":[0.9998017,0.0000554122,0.00003571619,0.00004206282,0.00005489071,0.00001020137],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001922765,0.00005204219,0.0005915642,0.0002044155,0.00006142843,0.0002170683,0.00007673137,0.03519917,0.4987724,0.003888212,0.001604284,0.4591403],"study_design_scores_gemma":[0.00001406771,0.0001340867,0.001860689,0.00001930502,0.00004774198,0.0009652795,0.00002377449,0.7230734,0.2647412,0.001870261,0.007220493,0.00002967487],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03295255,0.0009289517,0.9639722,0.00009695563,0.00003614805,0.00004214624,0.00007627202,0.0006834193,0.00121136],"genre_scores_gemma":[0.2507868,0.001024581,0.7438111,0.0001211446,0.00004842304,0.00005195391,0.0002830136,0.000101039,0.003771889],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00166565,"threshold_uncertainty_score":0.004841685,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008321808279812729,"score_gpt":0.2604784953613171,"score_spread":0.2521566870815044,"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."}}