{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000137976,0.0000795017,0.00007044844,0.00006014913,0.0001228787,0.0000629464,0.0002792003,0.00001638982,0.00004049235],"category_scores_gemma":[0.0000186992,0.0000717939,0.00001221355,0.0006617157,0.00002833674,0.0003834791,0.0002466074,0.00005477616,0.0002158957],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009751984,"about_ca_system_score_gemma":0.000009284242,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000113965,"about_ca_topic_score_gemma":0.00003493844,"domain_scores_codex":[0.9992061,0.00002612756,0.0001315131,0.0003285507,0.0001211932,0.0001864884],"domain_scores_gemma":[0.9992858,0.0001947963,0.00003029929,0.0003969219,0.00001535864,0.00007685269],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000009109332,0.00009892959,0.001962211,0.0000429895,0.00003355159,0.00009885564,0.0004433778,0.004941578,0.01789718,0.1324634,0.6218637,0.2201451],"study_design_scores_gemma":[0.0003991501,0.00003481042,0.005135949,0.000007593228,0.000006161698,0.00003807648,0.00003508112,0.9563985,0.002972236,0.008281747,0.02646462,0.0002260054],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005919806,0.00005290062,0.9865667,0.006279126,0.00009310986,0.000268705,0.00009406803,0.0004779798,0.0002476118],"genre_scores_gemma":[0.4290167,0.0003263523,0.563559,0.003399422,0.0002104683,0.0001773365,0.001983199,0.00002088928,0.001306609],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.951457,"threshold_uncertainty_score":0.292767,"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."}}