{"id":"W2991471181","doi":"10.1109/iccv.2019.00533","title":"Gated-SCNN: Gated Shape CNNs for Semantic Segmentation","year":2019,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":737,"is_retracted":false,"has_abstract":true,"ca_institutions":"Vector Institute; University of Toronto; University of Waterloo","funders":"","keywords":"Computer science; Segmentation; Artificial intelligence; Benchmark (surveying); Noise (video); Computer vision; Representation (politics); Focus (optics); Pattern recognition (psychology); Boundary (topology); Architecture; Image segmentation; Image (mathematics); 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006836867,0.0001017868,0.0001012246,0.00004755846,0.00008700415,0.00006266852,0.0004331172,0.00003504639,0.00009556585],"category_scores_gemma":[0.000006985197,0.00009176454,0.00004311914,0.0003810472,0.00001334417,0.0004109701,0.00009358166,0.00005323122,0.000535928],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003019785,"about_ca_system_score_gemma":0.00001874233,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003618987,"about_ca_topic_score_gemma":0.000004904115,"domain_scores_codex":[0.9990935,0.00001420126,0.0001731383,0.0003601057,0.0001260235,0.0002330451],"domain_scores_gemma":[0.9992352,0.0001253617,0.00007144559,0.0004335286,0.0000763189,0.00005811804],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003403328,0.0002345175,0.0023843,0.0001041675,0.00006459486,0.000003473926,0.0005592314,0.01719052,0.1965085,0.5442382,0.02135104,0.2173275],"study_design_scores_gemma":[0.0005213788,0.00008745215,0.0006463597,0.00001009702,0.000005927378,0.000006742953,0.00002330164,0.9488212,0.03028839,0.00752596,0.01185414,0.0002090502],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04566313,0.00001689283,0.9494368,0.00146454,0.0001875928,0.0008910551,0.000002103539,0.0003937503,0.001944174],"genre_scores_gemma":[0.7864669,0.00001016743,0.2090379,0.001060709,0.00004752585,0.00006734557,0.00002413901,0.00001448434,0.00327078],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9316307,"threshold_uncertainty_score":0.6888449,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0204940933020382,"score_gpt":0.2824852809530363,"score_spread":0.2619911876509981,"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."}}