DepMamba: Progressive Fusion Mamba for Multimodal Depression Detection
<|reference_start|>DepMamba: Progressive Fusion Mamba for Multimodal Depression Detection: 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.<|reference_end|>
@article{ye2024depmamba:,
    title={DepMamba: Progressive Fusion Mamba for Multimodal Depression Detection},
    author={Jiaxin Ye and Junping Zhang and Hongming Shan},
    journal={arXiv preprint arXiv:2409.15936},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.15936},
    primaryClass={cs.CY cs.CV cs.HC}
}