{"id":"W4385804870","doi":"10.1109/cvprw59228.2023.00556","title":"DACNet: A Deep Automated Checkout Network with Selective Deblurring","year":2023,"lang":"en","type":"article","venue":"","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Deblurring; Computer science; Deep learning; Artificial intelligence; Pipeline (software); Process (computing); Code (set theory); Training set; Tracking (education); Computer vision; Set (abstract data type); Object detection; Machine learning; Image (mathematics); Pattern recognition (psychology); Image restoration; Image processing","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":[0.0007828302,0.001285313,0.00074651,0.0005707135,0.0004392285,0.0007737115,0.002119107,0.001118949,0.004549341],"category_scores_gemma":[0.001874465,0.0005406159,0.0005746588,0.0003333614,0.0006539801,0.001134321,0.001393285,0.001606582,0.001904933],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009097899,"about_ca_system_score_gemma":0.001074892,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0108556,"about_ca_topic_score_gemma":0.0179799,"domain_scores_codex":[0.9997032,0.00003507521,0.000009902271,0.0001143786,0.00008272628,0.00005467821],"domain_scores_gemma":[0.9994301,0.0001766611,0.0000539081,0.0001316033,0.0001644309,0.00004330911],"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":[0.0006519293,0.0002778047,0.003107403,0.0001671855,0.0001612241,0.0002943712,0.0001247486,0.2257826,0.03232354,0.005044555,0.03259476,0.6994699],"study_design_scores_gemma":[0.00002479036,0.00008911549,0.0003963448,0.00001897693,0.00002118835,0.00006781211,0.00001403698,0.9789608,0.01466768,0.001876636,0.003845307,0.00001743122],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06655403,0.0010361,0.8934871,0.000480639,0.0004037038,0.0002670177,0.001113775,0.02972635,0.006931308],"genre_scores_gemma":[0.5311573,0.0004365146,0.4346875,0.0009587486,0.0001131324,0.0003024127,0.004563222,0.001188431,0.02659274],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0108556,"threshold_uncertainty_score":0.02158481,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009610696836931533,"score_gpt":0.222666805680246,"score_spread":0.2130561088433145,"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."}}