{"id":"W3192435669","doi":"10.1109/acii52823.2021.9597460","title":"Spatiotemporal Contrastive Learning of Facial Expressions in Videos","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Mitacs","keywords":"Computer science; Sampling (signal processing); Artificial intelligence; Scheme (mathematics); Pattern recognition (psychology); Facial expression; Sampling scheme; Deep learning; Speech recognition; Computer vision; Mathematics; Statistics","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":[0.0006792079,0.0006968224,0.0005121175,0.0005243284,0.0001503225,0.0004303384,0.000797496,0.0004128009,0.001696645],"category_scores_gemma":[0.002220017,0.0002032093,0.000538762,0.0003589,0.000378733,0.0008565439,0.0008296343,0.0009104186,0.0007003693],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003521452,"about_ca_system_score_gemma":0.0003525715,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001987714,"about_ca_topic_score_gemma":0.002676296,"domain_scores_codex":[0.9995649,0.0001156019,0.0000145232,0.0001534442,0.00009278237,0.00005867436],"domain_scores_gemma":[0.9996378,0.0001400357,0.00005159384,0.00006050943,0.00008465743,0.00002543507],"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":[0.0004674366,0.0002593762,0.003024284,0.0001523618,0.0000908737,0.000150462,0.0001197911,0.1035877,0.1097986,0.004565331,0.006252887,0.7715309],"study_design_scores_gemma":[0.00001066182,0.0001319062,0.001806153,0.00001408867,0.00001765816,0.0001070387,0.00002782276,0.9729326,0.02099795,0.002279063,0.001664147,0.00001090989],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09169641,0.0006863491,0.9001406,0.0003026467,0.0001207217,0.0001123454,0.0003795246,0.001556305,0.005005096],"genre_scores_gemma":[0.7662407,0.0006431483,0.2246136,0.0003170921,0.0001700311,0.0001448718,0.001000819,0.0001581573,0.006711737],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001987714,"threshold_uncertainty_score":0.005675852,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01938747326674388,"score_gpt":0.2701144699575886,"score_spread":0.2507269966908448,"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."}}