{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007854649,0.002407085,0.0009742396,0.003556454,0.0008839787,0.001013192,0.0026048,0.002017435,0.003568883],"category_scores_gemma":[0.002203301,0.0004520534,0.00135987,0.003558156,0.0008887152,0.001419855,0.0020444,0.001152931,0.003836318],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001227301,"about_ca_system_score_gemma":0.001390326,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02057526,"about_ca_topic_score_gemma":0.04344295,"domain_scores_codex":[0.9989311,0.00009754613,0.00009355815,0.0003689237,0.0003406695,0.0001681397],"domain_scores_gemma":[0.9992419,0.0001233555,0.00006922925,0.0002522883,0.0002181408,0.00009509477],"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":[0.001517408,0.00136548,0.01337518,0.005259769,0.0006337223,0.001286299,0.0004608057,0.0645007,0.05026333,0.00300595,0.4882774,0.3700538],"study_design_scores_gemma":[0.00075404,0.001182852,0.08736205,0.0008627469,0.0004874473,0.004889226,0.002398438,0.4136753,0.1179114,0.01131153,0.3587136,0.0004514097],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.3174917,0.007772291,0.0755735,0.001647236,0.001150669,0.002398552,0.5054634,0.06054927,0.02795351],"genre_scores_gemma":[0.1190081,0.0009523978,0.08876966,0.0003418217,0.00008983199,0.0005961164,0.785638,0.001348593,0.003255476],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.02057526,"threshold_uncertainty_score":0.04091096,"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."}}