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.

本文提出了一种多模态多阶因素融合 (MMFF) 方法，能够很好地利用不同模态之间的高阶交互作用，从而进行抑郁症的自动诊断，并且能够解释模型的融合机制。在两个最近流行的数据集上进行了广泛的实验证明，相比其他现有方法，我们的方法具有更好的性能。