  Feature alignment is the primary means of fusing multimodal data. We propose
a feature alignment method that fully fuses multimodal information, which
stepwise shifts and expands feature information from different modalities to
have a consistent representation in a feature space. The proposed method can
robustly capture high-level interactions between features of different
modalities, thus significantly improving the performance of multimodal
learning. We also show that the proposed method outperforms other popular
multimodal schemes on multiple tasks. Experimental evaluation of ETT and
MIT-BIH-Arrhythmia, datasets shows that the proposed method achieves state of
the art performance.
