{"id":"W4386076662","doi":"10.1109/cvpr52729.2023.01480","title":"Exemplar-FreeSOLO: Enhancing Unsupervised Instance Segmentation with Exemplars","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Institute for Advanced Research; Carleton University","funders":"","keywords":"Computer science; Segmentation; Artificial intelligence; Embedding; Discriminative model; Pattern recognition (psychology); Object (grammar); Annotation; Unsupervised learning; Image segmentation; Perspective (graphical); Abstraction; Segmentation-based object categorization; Scale-space segmentation; Machine learning","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":[0.0008230295,0.001644762,0.001536857,0.001787617,0.0004917024,0.001613988,0.002800356,0.001690559,0.002723323],"category_scores_gemma":[0.002047896,0.0007503945,0.001815081,0.001403619,0.001135343,0.00309516,0.002396221,0.001995507,0.001587126],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007496314,"about_ca_system_score_gemma":0.0008332172,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002973808,"about_ca_topic_score_gemma":0.007567619,"domain_scores_codex":[0.9993933,0.00008610543,0.00002835711,0.0002628173,0.0001518916,0.00007746035],"domain_scores_gemma":[0.999254,0.0002289082,0.00008309821,0.0002602898,0.0001158731,0.00005762828],"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":[0.0004503576,0.0003143733,0.003334233,0.00054474,0.0003918942,0.0003986114,0.0004651572,0.1913807,0.09500237,0.0159795,0.01198638,0.6797518],"study_design_scores_gemma":[0.0000204373,0.00009425278,0.0007878526,0.00002369303,0.00005014648,0.0001913227,0.00007300906,0.9546838,0.02914758,0.009598993,0.005296906,0.00003201326],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02890404,0.0004127157,0.9613004,0.0001300594,0.00004330767,0.0001068644,0.0004260651,0.006997047,0.001679576],"genre_scores_gemma":[0.2226067,0.0003833004,0.7682836,0.0004283576,0.00005145404,0.0001668931,0.003529889,0.001419819,0.003129972],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002973808,"threshold_uncertainty_score":0.009110451,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01753734228123322,"score_gpt":0.2582205701755576,"score_spread":0.2406832278943243,"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."}}