{"id":"W3192829953","doi":"10.1109/tip.2021.3102509","title":"MGSeg: Multiple Granularity-Based Real-Time Semantic Segmentation Network","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"Bank of Canada","funders":"Sichuan Province Science and Technology Support Program; National Natural Science Foundation of China","keywords":"Granularity; Computer science; Segmentation; Artificial intelligence; Pattern recognition (psychology); Benchmark (surveying); Feature (linguistics); Semantics (computer science); Feature extraction","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001444097,0.0002263946,0.0002133074,0.00009520378,0.0008359834,0.0003282848,0.0003725173,0.0000746466,0.00003367084],"category_scores_gemma":[0.000008119818,0.000251546,0.0001107882,0.001346986,0.00007436676,0.0009439878,0.000005013029,0.0002900332,0.0001177248],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000925257,"about_ca_system_score_gemma":0.0001869238,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008740397,"about_ca_topic_score_gemma":0.00002290541,"domain_scores_codex":[0.9981647,0.0001077547,0.0003342517,0.0006480027,0.0003224067,0.0004228498],"domain_scores_gemma":[0.9987044,0.0002228516,0.0001389746,0.0005784802,0.0002322346,0.0001230948],"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":[0.00003325472,0.0004884361,0.00003715199,0.0001327981,0.00002658203,0.00007069737,0.000296422,0.4010236,0.3382749,0.0001193097,0.0003371241,0.2591598],"study_design_scores_gemma":[0.0005688696,0.00003001765,0.00007473007,0.0001006202,0.00003179339,0.00002884904,0.00001671085,0.7475331,0.2498122,0.001347193,0.0001623944,0.0002934512],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002231946,0.00007602795,0.9952929,0.00100864,0.0002055589,0.000268913,0.000006661128,0.0006105126,0.0002988333],"genre_scores_gemma":[0.4671149,0.000031507,0.5318298,0.0005066558,0.0000735209,0.0001238498,0.00001334157,0.00003122209,0.0002751797],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4648829,"threshold_uncertainty_score":0.9999937,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01531937366871666,"score_gpt":0.2683782895688636,"score_spread":0.253058915900147,"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."}}