{"id":"W4220917622","doi":"10.1109/tnnls.2022.3153955","title":"Image Matting With Deep Gaussian Process","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks and Learning Systems","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Natural Science Foundation of Shandong Province; Taishan Scholar Foundation of Shandong Province; National Natural Science Foundation of China","keywords":"Artificial intelligence; Deep learning; Kernel (algebra); Image (mathematics); Computer science; Pixel; Scalability; Pattern recognition (psychology); Process (computing); Set (abstract data type); Gaussian process; Gaussian; Computer vision; Machine learning; Mathematics; Physics","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":[0.000358688,0.0001678844,0.0001707234,0.0001123286,0.001187828,0.0003104412,0.0003187947,0.00003063106,0.00001785824],"category_scores_gemma":[0.000001485678,0.0001467985,0.00003534952,0.0004310922,0.00003815788,0.0004043474,0.000006399742,0.0008544849,0.00000184122],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004166576,"about_ca_system_score_gemma":0.00001283607,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004094362,"about_ca_topic_score_gemma":0.000003753605,"domain_scores_codex":[0.9984913,0.0003032515,0.0002002609,0.0003909876,0.0002993949,0.0003148352],"domain_scores_gemma":[0.9994549,0.00008846531,0.0001251237,0.0002259473,0.0000379698,0.00006759715],"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":[0.00002329082,0.00003860444,0.00004468057,0.00003708687,0.00001602523,0.00004329509,0.0004799062,0.9771481,0.000182176,0.00005152881,0.00003962965,0.02189573],"study_design_scores_gemma":[0.0002128155,0.0004461127,0.00002906607,0.00004161978,0.000009045627,0.0001907894,0.0004185219,0.9979544,0.0001754578,0.000003625786,0.0003237836,0.0001947633],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0131949,0.0001283036,0.9848839,0.0001756056,0.0004267699,0.0003130478,6.116902e-7,0.0005899633,0.0002869088],"genre_scores_gemma":[0.9963596,0.000008590774,0.002726428,0.0001044666,0.00005485373,0.000245665,0.000001011353,0.00002417968,0.0004751407],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9831648,"threshold_uncertainty_score":0.9135932,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007203179155005628,"score_gpt":0.2251430017639678,"score_spread":0.2179398226089622,"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."}}