{"id":"W2888703449","doi":"10.1109/globalsip.2018.8646501","title":"Video Super-Resolution via Dynamic Local Filter Network","year":2018,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Artificial intelligence; Computer vision; Motion compensation; Compensation (psychology); Convolutional neural network; Autoencoder; Filter (signal processing); Optical flow; Frame (networking); Motion estimation; Pixel; Pattern recognition (psychology); Artificial neural network; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004138492,0.0004900507,0.0004434665,0.0005860098,0.0002183594,0.000367152,0.0006390571,0.0005372717,0.001410173],"category_scores_gemma":[0.0007082954,0.0002708508,0.0004203091,0.0005294164,0.000257242,0.0009818387,0.000562568,0.0006528224,0.000399751],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004977164,"about_ca_system_score_gemma":0.0003804552,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003185315,"about_ca_topic_score_gemma":0.005172275,"domain_scores_codex":[0.9997522,0.00003592143,0.00001067012,0.0000617737,0.000115093,0.00002421898],"domain_scores_gemma":[0.9998084,0.00006398821,0.00003349947,0.00002900543,0.00005022913,0.00001498172],"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":[0.0002738211,0.00008901262,0.0007578161,0.00019115,0.0001140511,0.0003492861,0.0001218989,0.08435433,0.4000565,0.006407528,0.00362647,0.5036582],"study_design_scores_gemma":[0.00001290413,0.00005514582,0.0006388561,0.00001108816,0.0000313843,0.0003712335,0.00001436478,0.9325356,0.06102086,0.001594163,0.003693651,0.00002074273],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02616592,0.0006803423,0.9703874,0.0001525549,0.00003963554,0.00003211771,0.00006837326,0.0008007695,0.001672845],"genre_scores_gemma":[0.2853686,0.001008863,0.7082862,0.0001964029,0.00006226483,0.00005540664,0.0002511732,0.0001005444,0.004670557],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003185315,"threshold_uncertainty_score":0.00633353,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008694242466371956,"score_gpt":0.2633832405441133,"score_spread":0.2546889980777414,"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."}}