{"id":"W2945490722","doi":"10.48550/arxiv.1905.05889","title":"DARNet: Deep Active Ray Network for Building Segmentation","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Canadian Institute for Advanced Research; Nvidia","keywords":"Computer science; Artificial intelligence; Segmentation; Polygon (computer graphics); Convolutional neural network; Function (biology); Deep learning; Active contour model; Energy (signal processing); Computer vision; Intersection (aeronautics); Energy minimization; Image segmentation; Pattern recognition (psychology); Mathematics; Cartography; Frame (networking)","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003950284,0.001767239,0.001013481,0.001270345,0.0003469639,0.001177127,0.002982436,0.001628624,0.005220163],"category_scores_gemma":[0.0008712552,0.0008826433,0.0009782512,0.0009091222,0.0005980025,0.001938287,0.001122732,0.001609271,0.002679028],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001145337,"about_ca_system_score_gemma":0.0009070569,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005803542,"about_ca_topic_score_gemma":0.01176201,"domain_scores_codex":[0.9996878,0.00003545899,0.00001096128,0.0001175135,0.00009456461,0.00005372233],"domain_scores_gemma":[0.9997712,0.00006139893,0.00003224659,0.00004966242,0.00006277085,0.00002261736],"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":[0.0003644598,0.0001868016,0.001395905,0.0002441974,0.0001726247,0.0002049088,0.0001058558,0.4917675,0.02039732,0.01038754,0.0174837,0.4572892],"study_design_scores_gemma":[0.000008511321,0.00003068159,0.0001854249,0.00001191676,0.00001397036,0.00004637582,0.000009519318,0.9872643,0.006217512,0.002981986,0.003219435,0.0000104241],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02802109,0.00125851,0.9452507,0.000360577,0.0002122093,0.0001221925,0.001143304,0.01675349,0.00687792],"genre_scores_gemma":[0.3725519,0.0009882246,0.5979508,0.0006957552,0.0001528823,0.0002586456,0.006851917,0.001302117,0.01924783],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005803542,"threshold_uncertainty_score":0.01746321,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05916007232131909,"score_gpt":0.2228668598872059,"score_spread":0.1637067875658869,"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."}}