  Feature alignment serves as the primary mechanism for fusing multimodal data.
We put forth a feature alignment approach that achieves full integration of
multimodal information. This is accomplished via an alternating process of
shifting and expanding feature representations across modalities to obtain a
consistent unified representation in a joint feature space. The proposed
technique can reliably capture high-level interplay between features
originating from distinct modalities. Consequently, substantial gains in
multimodal learning performance are attained. Additionally, we demonstrate the
superiority of our approach over other prevalent multimodal fusion schemes on a
range of tasks. Extensive experimental evaluation conducted on multimodal
datasets comprising time series, image, and text demonstrates that our method
achieves state-of-the-art results.
