{"id":"W7125772603","doi":"10.21428/594757db.4bf551e5","title":"Multimodal Emotion Recognition with Cross-Attentive Learning and Feature Fusion","year":2025,"lang":"en","type":"article","venue":"","topic":"Emotion and Mood Recognition","field":"Psychology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université TÉLUQ; Concordia University","funders":"","keywords":"Robustness (evolution); Salient; Emotion recognition; Feature (linguistics); Feature learning; Fuse (electrical); Fusion mechanism; Raw data; Deep learning","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001312845,0.0001102958,0.00009958102,0.0001452354,0.0001979928,0.00005529559,0.00002587161,0.0001599563,0.001350261],"category_scores_gemma":[0.00002769919,0.0000882535,0.00002926949,0.0001741602,0.00006605433,0.0001173613,0.00002123364,0.0002567267,0.0001819454],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001919875,"about_ca_system_score_gemma":0.00001033418,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005078308,"about_ca_topic_score_gemma":0.00004512449,"domain_scores_codex":[0.9992634,0.0001063301,0.0001047877,0.0002966968,0.00007746703,0.0001513132],"domain_scores_gemma":[0.9996449,0.00004590443,0.00005496362,0.0000675187,0.0001457702,0.00004091777],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0007594649,0.0003267791,0.1103066,0.00006084585,0.0001571651,0.00001858148,0.002062683,0.00000607772,0.00382999,0.001538041,0.004009999,0.8769238],"study_design_scores_gemma":[0.005731008,0.000526206,0.9709393,0.0002849845,0.00008283807,0.00007742377,0.005759896,0.0004712965,0.002445706,0.00124683,0.01205546,0.000379029],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8107716,0.00007543856,0.008202216,0.0006851743,0.0004054368,0.0002484173,0.000004570537,0.0001602006,0.1794469],"genre_scores_gemma":[0.9388109,0.0000391703,0.001141997,0.0003705199,0.00005475485,0.00002186492,0.0001485922,0.00001050647,0.05940168],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8765448,"threshold_uncertainty_score":0.9995626,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0162272330241502,"score_gpt":0.3211733410126636,"score_spread":0.3049461079885135,"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."}}