{"id":"W4293868328","doi":"10.1109/crv55824.2022.00018","title":"Adaptive Memory Management for Video Object Segmentation","year":2022,"lang":"en","type":"article","venue":"","topic":"Visual Attention and Saliency Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Computer vision; Segmentation; Object (grammar); Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002284648,0.00005044597,0.00004462908,0.0000907063,0.000301849,0.00004071765,0.0002212672,0.000005541591,0.0001473918],"category_scores_gemma":[0.000001868433,0.00004998105,0.00005128697,0.0002352981,0.000005307485,0.0002033733,0.0001702493,0.00003546617,0.00002378034],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000822173,"about_ca_system_score_gemma":0.000008831526,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001043453,"about_ca_topic_score_gemma":0.000003831852,"domain_scores_codex":[0.9993206,0.00004674471,0.0001041223,0.0002146565,0.0002067112,0.0001072097],"domain_scores_gemma":[0.9997588,0.00001654481,0.00003951439,0.0001398116,0.000022179,0.00002312038],"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":[0.00008595051,0.0003338663,0.00004802488,0.00003724536,0.00009329748,0.00001357905,0.001648405,0.005002724,0.00358217,0.4255901,0.02195914,0.5416055],"study_design_scores_gemma":[0.00330014,0.002370535,0.002577091,0.000007451815,0.00003896453,0.00004540528,0.01097729,0.8811246,0.02289822,0.03179523,0.04418916,0.0006758797],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002876491,0.000008901918,0.9823243,0.0002836583,0.0006268433,0.0004087331,0.000001651564,0.0001691293,0.01330028],"genre_scores_gemma":[0.9159063,0.000001575261,0.07132978,0.001711753,0.00002705087,0.0006404059,0.000005690796,0.000006101963,0.01037131],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9130298,"threshold_uncertainty_score":0.2321609,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02536455855276117,"score_gpt":0.2803962523940186,"score_spread":0.2550316938412574,"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."}}