{"id":"W3111262543","doi":"10.1109/tcsvt.2020.3042559","title":"Deep Variation Transformation Network for Foreground Detection","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Circuits and Systems for Video Technology","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Fundamental Research Funds for the Central Universities; Natural Sciences and Engineering Research Council of Canada; Chongqing Research Program of Basic Research and Frontier Technology; Chongqing Science and Technology Commission; National Natural Science Foundation of China","keywords":"Pixel; Artificial intelligence; Computer science; Pattern recognition (psychology); Foreground detection; Benchmark (surveying); Transformation (genetics); Deep learning; Computer vision; Variation (astronomy); Classifier (UML); Background subtraction","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0005242132,0.001299366,0.0009370552,0.0009424475,0.0003048559,0.000785188,0.001549397,0.0008325216,0.002064799],"category_scores_gemma":[0.001679104,0.0004730418,0.0008077556,0.001052978,0.0005654581,0.001159873,0.001007113,0.001433422,0.0006829772],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001254004,"about_ca_system_score_gemma":0.0008023351,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008083222,"about_ca_topic_score_gemma":0.009451148,"domain_scores_codex":[0.9995689,0.00006433612,0.00001510684,0.0001712741,0.0001052743,0.00007519467],"domain_scores_gemma":[0.9996527,0.0001249132,0.00005790491,0.00004988079,0.00009063014,0.00002384768],"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":[0.0002916373,0.0001059003,0.002596633,0.0001413097,0.0001332073,0.0002069299,0.00008629971,0.3861721,0.01304176,0.01072351,0.008034817,0.5784658],"study_design_scores_gemma":[0.000003343726,0.00001353635,0.0002360703,0.000005461116,0.000009559498,0.00003606942,0.000004345327,0.9935081,0.002016058,0.003533195,0.0006295121,0.0000047824],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02424602,0.001084896,0.9688454,0.000288808,0.00007750207,0.00004588059,0.000352356,0.00259136,0.002467868],"genre_scores_gemma":[0.7516519,0.001129688,0.2329059,0.0005050148,0.0001521652,0.0001193002,0.00257121,0.0003927484,0.01057197],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008083222,"threshold_uncertainty_score":0.01607233,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0344171522939143,"score_gpt":0.263994802236814,"score_spread":0.2295776499428997,"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."}}