{"id":"W3014967422","doi":"10.48550/arxiv.2004.00123","title":"EOLO: Embedded Object Segmentation only Look Once","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lakehead University","funders":"","keywords":"Segmentation; Computer science; Artificial intelligence; Task (project management); Embedding; Object (grammar); Pattern recognition (psychology); Scale-space segmentation; Computer vision; Machine learning; Image segmentation","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00008148128,0.0003262645,0.0002958989,0.0001293276,0.0002011284,0.0001061457,0.002048265,0.0001866494,0.00002298329],"category_scores_gemma":[0.00002366647,0.0004082242,0.0001713594,0.0009157697,0.00009879921,0.0005104371,0.002167893,0.0006218764,0.0003229423],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002678778,"about_ca_system_score_gemma":0.0002702944,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002658206,"about_ca_topic_score_gemma":0.00002898442,"domain_scores_codex":[0.9977574,0.0001191758,0.0002240467,0.00143898,0.0001190108,0.0003413521],"domain_scores_gemma":[0.997917,0.0001382034,0.000332936,0.001286225,0.0001164345,0.0002092077],"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":[0.00003331523,0.0000970445,0.0009210717,0.000069126,0.00009170432,0.0002912098,0.0005965981,0.6646116,0.001014351,0.3260687,0.001524311,0.004680975],"study_design_scores_gemma":[0.0005126562,0.00006838579,0.0008445916,0.00006283292,0.00006797591,0.00001102047,0.0001009058,0.8539054,0.001435697,0.1411703,0.001110201,0.0007101114],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04772313,0.00004665329,0.9477001,0.0005137384,0.000341258,0.0005466734,0.00001363415,0.0006109662,0.002503873],"genre_scores_gemma":[0.9621466,0.0001639544,0.03584787,0.000523008,0.0001030115,0.000004265882,0.0000449394,0.00002369784,0.001142594],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9144235,"threshold_uncertainty_score":0.999837,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07316238969611112,"score_gpt":0.2192949167725961,"score_spread":0.1461325270764849,"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."}}