DepMamba: Progressive Fusion Mamba for Multimodal Depression Detection
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866916409509937152 |
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| author | Ye, Jiaxin Zhang, Junping Shan, Hongming |
| author_facet | Ye, Jiaxin Zhang, Junping Shan, Hongming |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_15936 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | DepMamba: Progressive Fusion Mamba for Multimodal Depression Detection Ye, Jiaxin Zhang, Junping Shan, Hongming Computers and Society Computer Vision and Pattern Recognition Human-Computer Interaction 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. |
| title | DepMamba: Progressive Fusion Mamba for Multimodal Depression Detection |
| topic | Computers and Society Computer Vision and Pattern Recognition Human-Computer Interaction |
| url | https://arxiv.org/abs/2409.15936 |