{"id":"W2964191931","doi":"","title":"Unsupervised Video Object Segmentation for Deep Reinforcement Learning","year":2018,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Reinforcement Learning in Robotics","field":"Computer Science","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Reinforcement learning; Computer science; Artificial intelligence; Segmentation; Representation (politics); Object (grammar); Motion (physics); Code (set theory); Unsupervised learning; Object detection; Focus (optics); Computer vision; Action (physics); Suite; Feature learning; Deep learning; Machine learning; Set (abstract data type)","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":[0.001026017,0.0008856543,0.0007984735,0.0004890698,0.0003119605,0.0006768045,0.001416853,0.001001106,0.004240522],"category_scores_gemma":[0.003826748,0.0004892582,0.0004999338,0.0004340155,0.0009752417,0.001155134,0.0009736351,0.001769189,0.0006532142],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001827391,"about_ca_system_score_gemma":0.001330127,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0057501,"about_ca_topic_score_gemma":0.006056781,"domain_scores_codex":[0.9996127,0.000105652,0.00001762087,0.0001190091,0.00009695398,0.00004797538],"domain_scores_gemma":[0.9990675,0.000510788,0.0001279741,0.0001211427,0.0001150169,0.00005751651],"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":[0.0001038108,0.0000803748,0.000589411,0.00008298709,0.00004291433,0.00006175187,0.00004493542,0.8732145,0.003962018,0.02903226,0.002469751,0.09031525],"study_design_scores_gemma":[0.000005298687,0.000009687232,0.00003486225,0.000003917947,0.000001982026,0.000004768802,0.000001389281,0.9910782,0.0005477325,0.007882025,0.0004276913,0.000002385],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005626094,0.0001463057,0.9915643,0.0001390132,0.0000293503,0.00003522583,0.0000575982,0.0009699704,0.001432126],"genre_scores_gemma":[0.5709888,0.0002685237,0.4224313,0.0002545559,0.00006384393,0.0003166735,0.0003915426,0.0003217368,0.00496288],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0057501,"threshold_uncertainty_score":0.01418591,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0505535799427427,"score_gpt":0.202873746225006,"score_spread":0.1523201662822633,"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."}}