UF-AMA fuses EEG and eye-tracking via transformers and cross-attention, applies confidence-based screening with alignment and distillation, and uses multi-level domain adaptation to reach SOTA on cross-subject and cross-session tasks in SEED and SEED-IV datasets.
Maximum mean discrepancy for gener- alization in the presence of distribution and missingness shift
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UF-AMA: A unified framework for cross-domain emotion recognition via adaptive multimodal alignment
UF-AMA fuses EEG and eye-tracking via transformers and cross-attention, applies confidence-based screening with alignment and distillation, and uses multi-level domain adaptation to reach SOTA on cross-subject and cross-session tasks in SEED and SEED-IV datasets.