{"id":"W4415530757","doi":"10.1016/j.neunet.2025.108232","title":"Internal-external boundary attention fusion for glass surface segmentation","year":2025,"lang":"en","type":"article","venue":"Neural Networks","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Information Technology Research Centre; Ministry of Science and ICT, South Korea; Institute for Information and Communications Technology Promotion; Electronics and Telecommunications Research Institute; National Natural Science Foundation of China","keywords":"Boundary (topology); Benchmark (surveying); Surface (topology); Fusion; Task (project management); Segmentation; Exploit","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001659201,0.0001196532,0.0001328887,0.00005924199,0.0001303443,0.0001110867,0.00006808445,0.0001383258,0.00001704774],"category_scores_gemma":[0.000008571586,0.0001148718,0.00009944,0.0001678377,0.00001031548,0.000143977,0.00002029985,0.0001787641,0.000006152413],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009394799,"about_ca_system_score_gemma":0.000005482954,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003385967,"about_ca_topic_score_gemma":0.00001644606,"domain_scores_codex":[0.9992722,0.00003291109,0.000257472,0.0001507348,0.000101941,0.0001847177],"domain_scores_gemma":[0.9997106,0.0000604173,0.00004322485,0.0001062613,0.00004677689,0.00003273784],"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":[0.000218186,0.00001656058,0.00289839,0.00008649309,0.00004969943,0.000003170977,0.00003415716,0.7141524,0.05195977,0.00009224738,0.0222113,0.2082776],"study_design_scores_gemma":[0.0007697039,0.00006846087,0.002679048,0.0001208783,0.00001943852,0.000005319484,0.00002853295,0.9868759,0.002682782,0.00007338357,0.006560082,0.0001164751],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5740778,0.0003275835,0.4189429,0.00003060059,0.005503456,0.0003342594,0.000003310791,0.0001832326,0.0005968633],"genre_scores_gemma":[0.9981176,0.00002503044,0.0002242977,0.00006190405,0.0005349934,0.00002421941,0.00001894237,0.0000177274,0.0009752878],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4240398,"threshold_uncertainty_score":0.4684334,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01166002093370501,"score_gpt":0.2523567421843928,"score_spread":0.2406967212506878,"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."}}