{"id":"W3014111670","doi":"10.1109/iros45743.2020.9340821","title":"Semantic Segmentation of Underwater Imagery: Dataset and Benchmark","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"McGill University; University of Minnesota; National Science Foundation","keywords":"Computer science; Benchmark (surveying); Underwater; Artificial intelligence; Segmentation; Pipeline (software); Robot; Inference; Computer vision; Geography","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":[0.0001833057,0.0001700898,0.0002274927,0.00009357701,0.0000248041,0.0001648336,0.000664154,0.00007193088,0.00005965546],"category_scores_gemma":[0.00001617191,0.0001533003,0.00003115108,0.00009005598,0.00005257185,0.000346963,0.002751557,0.000172962,0.00001143434],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002365357,"about_ca_system_score_gemma":0.00004738332,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009301602,"about_ca_topic_score_gemma":0.000004951695,"domain_scores_codex":[0.9987859,0.00005415593,0.0002768651,0.0005136165,0.000235922,0.000133498],"domain_scores_gemma":[0.9990659,0.00004361044,0.0001612048,0.0006421335,0.00004228181,0.0000448106],"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":[0.00002496839,0.0002638073,0.001312481,0.003305844,0.0003139168,0.0001125143,0.002397522,0.00003794821,0.3681118,0.01853445,0.5437729,0.06181189],"study_design_scores_gemma":[0.0002381706,0.0001090256,0.0007754986,0.0001619438,0.00004361997,0.000007366632,0.00003034603,0.0316092,0.9259033,0.03938185,0.001251226,0.0004884831],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00122899,0.00005466419,0.9953145,0.00155709,0.0001280749,0.0003640874,0.0001503852,0.0001862827,0.001015951],"genre_scores_gemma":[0.3538738,0.0001282086,0.6432472,0.0008395768,0.00003506295,0.0000391007,0.001717726,0.00001268572,0.0001065722],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.5577915,"threshold_uncertainty_score":0.6251402,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02547458921488926,"score_gpt":0.2884695728503873,"score_spread":0.262994983635498,"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."}}