{"id":"W3136263466","doi":"10.1109/cvprw53098.2021.00317","title":"Video Class Agnostic Segmentation Benchmark for Autonomous Driving","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Segmentation; Computer science; Benchmark (surveying); Artificial intelligence; Class (philosophy); Computer vision; Set (abstract data type); Task (project management); Baseline (sea); Motion (physics); Scale-space segmentation; Robotics; Image segmentation; Machine learning; Robot","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":[0.000135984,0.0002609335,0.0002688208,0.00008452023,0.000188762,0.0003922578,0.001090903,0.0001621875,0.00003309845],"category_scores_gemma":[0.0001048437,0.0002792434,0.0001596483,0.0002571199,0.00002829804,0.0003376801,0.001586565,0.0003075191,0.00001622274],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000200815,"about_ca_system_score_gemma":0.0002068591,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001159741,"about_ca_topic_score_gemma":0.00005894592,"domain_scores_codex":[0.998009,0.0000457831,0.0003989808,0.001003483,0.0002097368,0.0003330414],"domain_scores_gemma":[0.9975847,0.0007440025,0.0002426087,0.001143103,0.0001781654,0.0001074194],"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":[0.000006191686,0.0003025161,0.0007128619,0.0003869588,0.0001808272,0.00002692424,0.00135027,0.4672181,0.009314789,0.1635288,0.01845315,0.3385186],"study_design_scores_gemma":[0.0003349049,0.00005171943,0.001610276,0.0001663107,0.00004875602,0.00001628682,0.0000540803,0.9291781,0.009961529,0.05037812,0.007408424,0.0007915285],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003908477,0.0001598487,0.9897478,0.002196403,0.0009063652,0.00117839,0.000006357385,0.0004198097,0.001476588],"genre_scores_gemma":[0.2654545,0.00007233951,0.7312382,0.0007508484,0.0002183568,0.001319164,0.000207778,0.00002493077,0.0007138737],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.46196,"threshold_uncertainty_score":0.999966,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02141733507135049,"score_gpt":0.2901025194071777,"score_spread":0.2686851843358272,"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."}}