{"id":"W4312438838","doi":"10.1109/iros47612.2022.9982252","title":"Towards Inclusive HRI: Using Sim2Real to Address Underrepresentation in Emotion Expression Recognition","year":2022,"lang":"en","type":"article","venue":"2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","topic":"Emotion and Mood Recognition","field":"Psychology","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Artificial intelligence; Facial expression; Population; Machine learning; Benchmark (surveying); Set (abstract data type); Facial recognition system; Face (sociological concept); Perception; Suite; Expression (computer science); Feature extraction; Human–computer interaction; Psychology","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0006821855,0.0002795951,0.0003109535,0.0007938275,0.0003436094,0.0001665827,0.0002989505,0.0001397621,0.005026727],"category_scores_gemma":[0.0000628655,0.0002929326,0.0000934437,0.0003336251,0.0000460412,0.0002180832,0.000218062,0.0004794053,0.0001623077],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00056839,"about_ca_system_score_gemma":0.00007920967,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001732433,"about_ca_topic_score_gemma":0.0001336355,"domain_scores_codex":[0.9966969,0.0006624594,0.0007420992,0.0007459504,0.0008228432,0.0003297806],"domain_scores_gemma":[0.9988773,0.00008873261,0.0003459183,0.00024023,0.0002765515,0.0001712294],"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":[0.006907249,0.008860027,0.01033932,0.0005034815,0.001045924,0.000750817,0.09827577,0.1899252,0.1986866,0.1392612,0.03004426,0.3154002],"study_design_scores_gemma":[0.01526338,0.006345606,0.03609839,0.005258219,0.0003366843,0.001647229,0.3602708,0.4865676,0.04239837,0.02528364,0.0144318,0.006098229],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9125202,0.0001167043,0.03235705,0.00120152,0.01227036,0.001783003,0.0003882559,0.0001131526,0.03924971],"genre_scores_gemma":[0.995668,0.00006902857,0.00008735862,0.0004795511,0.0003664687,0.0004518518,0.0005870993,0.00003497152,0.002255666],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.309302,"threshold_uncertainty_score":0.9999523,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2072147991692482,"score_gpt":0.4137955656372383,"score_spread":0.2065807664679901,"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."}}