DepMamba: Progressive Fusion Mamba for Multimodal Depression Detection
This work addresses depression detection, a critical mental health issue, by improving multimodal fusion methods, though it appears incremental as it builds on existing Mamba and fusion techniques.
The paper tackles inefficient long-range temporal modeling and sub-optimal multimodal fusion in depression detection by proposing DepMamba, a progressive fusion Mamba model that achieves superior performance over state-of-the-art methods on two large-scale datasets.
Depression is a common mental disorder that affects millions of people worldwide. Although promising, current multimodal methods hinge on aligned or aggregated multimodal fusion, suffering two significant limitations: (i) inefficient long-range temporal modeling, and (ii) sub-optimal multimodal fusion between intermodal fusion and intramodal processing. In this paper, we propose an audio-visual progressive fusion Mamba for multimodal depression detection, termed DepMamba. DepMamba features two core designs: hierarchical contextual modeling and progressive multimodal fusion. On the one hand, hierarchical modeling introduces convolution neural networks and Mamba to extract the local-to-global features within long-range sequences. On the other hand, the progressive fusion first presents a multimodal collaborative State Space Model (SSM) extracting intermodal and intramodal information for each modality, and then utilizes a multimodal enhanced SSM for modality cohesion. Extensive experimental results on two large-scale depression datasets demonstrate the superior performance of our DepMamba over existing state-of-the-art methods. Code is available at https://github.com/Jiaxin-Ye/DepMamba.