{"id":"W2966398006","doi":"10.1109/crv.2019.00026","title":"Instance Segmentation Based Semantic Matting for Compositing Applications","year":2019,"lang":"en","type":"article","venue":"","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Compositing; Computer science; Artificial intelligence; Computer vision; Segmentation; Image (mathematics); Task (project management); Image segmentation; Keying; Computer graphics (images)","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":[0.0003659924,0.001261186,0.0007302862,0.001140037,0.0003430811,0.001412424,0.001456498,0.0009347858,0.004499027],"category_scores_gemma":[0.001230168,0.0004359085,0.001065735,0.0012277,0.0004402396,0.00167022,0.0007973478,0.00116865,0.001839841],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004285331,"about_ca_system_score_gemma":0.0002705284,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009065519,"about_ca_topic_score_gemma":0.001594587,"domain_scores_codex":[0.9996061,0.00005528356,0.00002683177,0.0001212496,0.0001477144,0.00004288555],"domain_scores_gemma":[0.9994441,0.000166937,0.0000462724,0.0001927546,0.0001075885,0.00004242118],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0005453441,0.0001409855,0.0009862643,0.0004190171,0.0001254317,0.000507231,0.0003533476,0.03358395,0.2857711,0.01148124,0.008574177,0.6575119],"study_design_scores_gemma":[0.00002301851,0.0001176854,0.001180089,0.00002365565,0.00005889608,0.000786046,0.0001290808,0.7164124,0.248608,0.01191119,0.02070575,0.00004422038],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01083394,0.0001913926,0.9764357,0.0000403034,0.00003105315,0.00005545218,0.0001452463,0.01068095,0.00158591],"genre_scores_gemma":[0.1377616,0.0002832939,0.8571278,0.00006987537,0.00004586623,0.00005585452,0.0008858682,0.001573102,0.002196708],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004499027,"threshold_uncertainty_score":0.01505071,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009102799490976402,"score_gpt":0.2681075259153567,"score_spread":0.2590047264243803,"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."}}