CANAMRF: An Attention-Based Model for Multimodal Depression Detection
Yuntao Wei Yuzhe Zhang Shuyang Zhang Hong Zhang
Multimodal depression detection is an important research topic that aims to
predict human mental states using multimodal data. Previous methods treat
different modalities equally and fuse each modality by na\"ive mathematical
operations without measuring the relative importance between them, which cannot
obtain well-performed multimodal representations for downstream depression
tasks. In order to tackle the aforementioned concern, we present a Cross-modal
Attention Network with Adaptive Multi-modal Recurrent Fusion (CANAMRF) for
multimodal depression detection. CANAMRF is constructed by a multimodal feature
extractor, an Adaptive Multimodal Recurrent Fusion module, and a Hybrid
Attention Module. Through experimentation on two benchmark datasets, CANAMRF
demonstrates state-of-the-art performance, underscoring the effectiveness of
our proposed approach.