{"id":"W2939498466","doi":"10.1109/tcsvt.2020.2984241","title":"Learned 3D Shape Representations Using Fused Geometrically Augmented Images: Application to Facial Expression and Action Unit Detection","year":2020,"lang":"en","type":"preprint","venue":"IEEE Transactions on Circuits and Systems for Video Technology","topic":"Face recognition and analysis","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Vector Institute; University of Toronto","funders":"","keywords":"Artificial intelligence; Computer science; Discriminative model; Pattern recognition (psychology); Feature (linguistics); Representation (politics); Convolutional neural network; Facial expression; Expression (computer science); Image (mathematics); Property (philosophy); Computer vision","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":[0.0003545526,0.0006791601,0.0006462286,0.0009194249,0.0001315572,0.0005036079,0.0008990912,0.0007262024,0.001460811],"category_scores_gemma":[0.001015417,0.0002608564,0.0007010925,0.0007447319,0.0004160113,0.0007637081,0.0009408434,0.0007488372,0.0005846179],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003402457,"about_ca_system_score_gemma":0.0002964349,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001500763,"about_ca_topic_score_gemma":0.001782347,"domain_scores_codex":[0.9997211,0.00003778847,0.000008722428,0.00007899149,0.0001141106,0.0000393634],"domain_scores_gemma":[0.9997754,0.00005124861,0.0000315217,0.00007108227,0.00005503734,0.00001580803],"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":[0.0002347303,0.000124288,0.002235987,0.0001195641,0.0001033395,0.0001898726,0.0001009468,0.104537,0.1096282,0.003751996,0.003144649,0.7758294],"study_design_scores_gemma":[0.000007743807,0.00006947939,0.001745145,0.00001010217,0.00002099137,0.0002037763,0.00004784296,0.9757156,0.01678332,0.003970288,0.001411524,0.00001423184],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06246697,0.0003731295,0.9335706,0.0001847754,0.00005646548,0.00006499633,0.0002912622,0.001318001,0.001673862],"genre_scores_gemma":[0.5460423,0.0004520315,0.4500448,0.0001704112,0.00006878062,0.0000770084,0.0009869689,0.0001440498,0.002013568],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001500763,"threshold_uncertainty_score":0.004886925,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08211408856296784,"score_gpt":0.3243767265492116,"score_spread":0.2422626379862437,"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."}}