At the high level, MulT merges multimodal timeseries via a feed-forward fusion process from multiple directional pairwise crossmodal transformers.
each crossmodal transformer (introduced in Section 3.2) serves to repeatedly reinforce a target modality with the low-level features from another source modality by learning the attention across the two modalities' features.