{"id":"W4200099838","doi":"10.32920/17312189","title":"Unsupervised Panoptic Segmentation","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Image Processing Techniques and Applications","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"University of Toronto","keywords":"Panopticon; Computer science; Segmentation; Cluster analysis; Artificial intelligence; Visual cortex; Unsupervised learning; Code (set theory); Set (abstract data type); Image segmentation; Computer vision; Image (mathematics); Pattern recognition (psychology); Psychology; Neuroscience; Programming language","routes":{"ca_aff":true,"ca_fund":true,"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":[0.000673069,0.0008647809,0.001020217,0.002254253,0.0009124002,0.002008131,0.001944684,0.001395534,0.00824073],"category_scores_gemma":[0.002228193,0.0006923635,0.001225894,0.00218205,0.001263077,0.002927485,0.002912761,0.001498526,0.003365715],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008167805,"about_ca_system_score_gemma":0.001046213,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002428167,"about_ca_topic_score_gemma":0.003858654,"domain_scores_codex":[0.998865,0.0001342817,0.00006129509,0.0004770392,0.0003312643,0.0001310835],"domain_scores_gemma":[0.9987106,0.0002707204,0.000123148,0.0004805237,0.0003419355,0.00007305544],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005566592,0.0001485764,0.001667632,0.0006663398,0.0001719506,0.0003135483,0.0005928951,0.07556204,0.1207892,0.05313957,0.02007514,0.7263164],"study_design_scores_gemma":[0.00004197088,0.0001210534,0.003547288,0.0001276646,0.00007308461,0.0006503136,0.0002692888,0.8015714,0.06419583,0.06894607,0.06037309,0.00008296243],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008992686,0.0004498444,0.9823114,0.0001489584,0.00009156408,0.00009611881,0.0003920951,0.001921445,0.005595957],"genre_scores_gemma":[0.1442841,0.0008593156,0.8337433,0.0003142573,0.000246822,0.0002763129,0.003795983,0.001856558,0.01462338],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00824073,"threshold_uncertainty_score":0.02756798,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01685564448515926,"score_gpt":0.2556399365493213,"score_spread":0.238784292064162,"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."}}