{"id":"W4386071619","doi":"10.1109/cvpr52729.2023.00112","title":"Multispectral Video Semantic Segmentation: A Benchmark Dataset and Baseline","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"National Natural Science Foundation of China","keywords":"Multispectral image; Computer science; Segmentation; RGB color model; Artificial intelligence; Benchmark (surveying); Computer vision; Baseline (sea); Image segmentation; Focus (optics); Scale-space segmentation; Pixel; Segmentation-based object categorization; Semantics (computer science); Cartography; Geography","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":[0.001268963,0.00539961,0.001673906,0.006203611,0.001433589,0.0023114,0.004151888,0.003694101,0.005455809],"category_scores_gemma":[0.002938118,0.0005032643,0.002331143,0.005850174,0.001106816,0.002988528,0.002512027,0.001897977,0.006149756],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003107021,"about_ca_system_score_gemma":0.001863206,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03216775,"about_ca_topic_score_gemma":0.05547097,"domain_scores_codex":[0.9980834,0.0001822512,0.0001561113,0.0008318538,0.0005049381,0.00024146],"domain_scores_gemma":[0.9990459,0.0001411874,0.0001034289,0.0003002949,0.0002840938,0.0001251155],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002310587,0.002174638,0.007932087,0.005827853,0.0007776912,0.001380653,0.0003780082,0.03850363,0.02970504,0.004606466,0.5177245,0.3886788],"study_design_scores_gemma":[0.0008926162,0.001727333,0.04957729,0.001663369,0.0007276802,0.005629266,0.002233797,0.3484119,0.0695377,0.01513044,0.5039843,0.0004843275],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.1843777,0.01650439,0.05072959,0.001980913,0.001920458,0.003384943,0.6565346,0.05569337,0.02887392],"genre_scores_gemma":[0.04956934,0.00162081,0.05175158,0.0003993859,0.0001525075,0.0005940839,0.8919414,0.0007564297,0.003214404],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.03216775,"threshold_uncertainty_score":0.06396097,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02147292982552578,"score_gpt":0.2973099770474439,"score_spread":0.2758370472219181,"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."}}