{"id":"W3177126889","doi":"10.1109/cvpr46437.2021.01598","title":"Memory-Efficient Network for Large-scale Video Compressive Sensing","year":2021,"lang":"en","type":"article","venue":"","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":71,"is_retracted":false,"has_abstract":true,"ca_institutions":"Bell (Canada)","funders":"","keywords":"Computer science; Snapshot (computer storage); Artificial intelligence; Convolutional neural network; Deep learning; Computer vision; Grayscale; Compressed sensing; Pixel","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":[0.000086938,0.0001556153,0.0002172067,0.00002929527,0.0001223401,0.0000551235,0.00007797864,0.0000813137,0.00005750947],"category_scores_gemma":[0.00001860314,0.0001566187,0.0001069431,0.0001510648,0.00001417358,0.00003158366,0.00006269316,0.0001114905,0.00001418861],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000310479,"about_ca_system_score_gemma":0.00001538346,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005278157,"about_ca_topic_score_gemma":0.00003669508,"domain_scores_codex":[0.9990684,0.0000226817,0.0001833292,0.0002186685,0.0001105207,0.0003963887],"domain_scores_gemma":[0.9993306,0.0001125195,0.00002371331,0.0003221562,0.0001441187,0.00006690234],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001407942,0.00004836674,0.00002207146,0.00004079777,0.0001018548,0.00007080002,0.0003496249,0.7589357,0.02723737,0.001213832,0.1978581,0.01410734],"study_design_scores_gemma":[0.000240358,0.000009829794,0.00004868258,0.00007735021,0.00002165037,0.0000239065,0.000152747,0.6668314,0.2998554,0.0007625447,0.03177283,0.0002033139],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03280515,0.001004164,0.9408054,0.0001066496,0.0007782746,0.0002520131,0.0000135024,0.001509448,0.02272538],"genre_scores_gemma":[0.8634217,0.00003053037,0.134965,0.0004506957,0.0004664137,0.000009306431,0.0000322596,0.00006101836,0.0005631015],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8306165,"threshold_uncertainty_score":0.6386724,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01330462468956041,"score_gpt":0.2332690153180525,"score_spread":0.2199643906284921,"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."}}