{"id":"W4312258650","doi":"10.1109/igarss46834.2022.9883974","title":"Dpnet: end-to-end Aerial Image Segmentation Via Deformable Point Network","year":2022,"lang":"en","type":"article","venue":"IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Wilfrid Laurier University","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Artificial intelligence; Computer science; Segmentation; Computer vision; Discriminative model; Pyramid (geometry); Feature (linguistics); Clutter; Pattern recognition (psychology); Image segmentation; Robustness (evolution); 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002865075,0.001548107,0.0007454118,0.0009531608,0.0004521396,0.0007674309,0.002088103,0.001305744,0.006567916],"category_scores_gemma":[0.0006827576,0.0005970485,0.0005813767,0.0009090356,0.0004090361,0.001459955,0.001174939,0.001248722,0.002741912],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001040696,"about_ca_system_score_gemma":0.0008568186,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01695372,"about_ca_topic_score_gemma":0.02584292,"domain_scores_codex":[0.9997831,0.00001264394,0.000007449327,0.0001031772,0.00005608065,0.00003745358],"domain_scores_gemma":[0.9998374,0.00002769025,0.00001605765,0.00004713465,0.00005235428,0.0000192818],"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":[0.0003832629,0.0001966196,0.00140866,0.0001183184,0.0001509908,0.0003371253,0.00009177857,0.2590905,0.03256441,0.003297091,0.02680903,0.6755522],"study_design_scores_gemma":[0.00001155821,0.00002947615,0.0003502335,0.000004196333,0.000008819036,0.00004592351,0.00001396469,0.9881231,0.006961728,0.002467461,0.001974394,0.000009199604],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0424177,0.0005302117,0.9203178,0.0003574631,0.0002417378,0.0002487874,0.001631971,0.02833491,0.005919483],"genre_scores_gemma":[0.4139732,0.0004126216,0.5597218,0.0004811095,0.0001109169,0.0002765511,0.007404529,0.0008199002,0.01679933],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01695372,"threshold_uncertainty_score":0.03371006,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01021134635276539,"score_gpt":0.2574292105289875,"score_spread":0.2472178641762221,"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."}}