{"id":"W2943142229","doi":"10.1109/cvpr.2019.00594","title":"Deep Video Inpainting","year":2019,"lang":"en","type":"preprint","venue":"","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Inpainting; Computer science; Artificial intelligence; Computer vision; Encoder; Retargeting; Deep learning; Image (mathematics); Domain (mathematical analysis); Task (project management); Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003875218,0.000238704,0.0002986667,0.00008836652,0.00007409801,0.0004897955,0.001433509,0.0001841475,0.0001238498],"category_scores_gemma":[0.00008329475,0.0002044994,0.000171929,0.000129199,0.00002194855,0.0002405803,0.003254214,0.0003849525,0.0003718996],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004131403,"about_ca_system_score_gemma":0.00009889973,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000837593,"about_ca_topic_score_gemma":0.0000155285,"domain_scores_codex":[0.9983858,0.0001233145,0.0002540436,0.0007042506,0.0002284476,0.0003041246],"domain_scores_gemma":[0.9983957,0.0001360454,0.0001493348,0.001134152,0.0001154727,0.00006926756],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000003502525,0.00006484605,0.0006620851,0.0001109507,0.0001696736,0.00001946031,0.0007879946,0.7073869,0.0004627174,0.02876315,0.01919243,0.2423763],"study_design_scores_gemma":[0.00007178618,0.00001150187,0.0002700859,0.00004114658,0.000007236277,0.000001425408,0.00001256045,0.9872751,0.001117579,0.005176786,0.00572403,0.0002907976],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0001602451,0.0002236761,0.954169,0.0009750912,0.002106264,0.0002156799,5.160227e-7,0.0001945068,0.04195505],"genre_scores_gemma":[0.632629,0.00003397259,0.3637833,0.0009383123,0.0004445669,0.00001534626,0.000004480733,0.00001533771,0.002135671],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6324688,"threshold_uncertainty_score":0.8339245,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0157302261095906,"score_gpt":0.2317937077509374,"score_spread":0.2160634816413468,"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."}}