  Depression is a leading cause of death worldwide, and the diagnosis of
depression is nontrivial. Multimodal learning is a popular solution for
automatic diagnosis of depression, and the existing works suffer two main
drawbacks: 1) the high-order interactions between different modalities can not
be well exploited; and 2) interpretability of the models are weak. To remedy
these drawbacks, we propose a multimodal multi-order factor fusion (MMFF)
method. Our method can well exploit the high-order interactions between
different modalities by extracting and assembling modality factors under the
guide of a shared latent proxy. We conduct extensive experiments on two recent
and popular datasets, E-DAIC-WOZ and CMDC, and the results show that our method
achieve significantly better performance compared with other existing
approaches. Besides, by analyzing the process of factor assembly, our model can
intuitively show the contribution of each factor. This helps us understand the
fusion mechanism.
