{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006115122,0.0007873432,0.0008041158,0.001095362,0.0005582668,0.000726928,0.002535307,0.0008963443,0.003086518],"category_scores_gemma":[0.002744769,0.0003995977,0.000430022,0.001190496,0.0004368767,0.002481087,0.001058281,0.0007712003,0.0007813296],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001343311,"about_ca_system_score_gemma":0.0009274871,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01357771,"about_ca_topic_score_gemma":0.01536179,"domain_scores_codex":[0.9996407,0.00004324468,0.00002515181,0.0001601668,0.00007101592,0.00005971973],"domain_scores_gemma":[0.9993979,0.0002151198,0.00007938328,0.0001507614,0.0001135229,0.00004340273],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0006541506,0.0001874205,0.001772131,0.0001153236,0.00007980174,0.0001471252,0.0001856744,0.06480271,0.03303374,0.004670919,0.006599077,0.8877519],"study_design_scores_gemma":[0.00002966033,0.00009015439,0.0008689657,0.00001289248,0.00004087613,0.0001038805,0.00005656134,0.9654112,0.02253527,0.007665875,0.003165949,0.00001862994],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1052793,0.002443939,0.8781938,0.0003370633,0.000168383,0.0001689508,0.0004752933,0.00922064,0.003712629],"genre_scores_gemma":[0.7391071,0.0005805416,0.2545149,0.0002681195,0.0001029702,0.0001389225,0.0007601961,0.0002085512,0.004318662],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01357771,"threshold_uncertainty_score":0.02699733,"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."}}