DepMamba: Progressive Fusion Mamba for Multimodal Depression Detection

• URL: http://arxiv.org/abs/2409.15936v1
• Date: Tue, 24 Sep 2024 09:58:07 GMT
• Title: DepMamba: Progressive Fusion Mamba for Multimodal Depression Detection
• Authors: Jiaxin Ye, Junping Zhang, Hongming Shan,
• Abstract summary: Depression is a common mental disorder that affects millions of people worldwide. We propose an audio-visual progressive fusion Mamba for multimodal depression detection, termed DepMamba.
• Score: 37.701518424351505
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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