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CANAMRF: An Attention-Based Model for Multimodal Depression Detection
- Publication Year :
- 2024
-
Abstract
- 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.<br />Comment: 6 pages, 3 figures. Pacific Rim International Conference on Artificial Intelligence. Singapore: Springer Nature Singapore, 2023
Details
- Database :
- arXiv
- Publication Type :
- Report
- Accession number :
- edsarx.2401.02995
- Document Type :
- Working Paper
- Full Text :
- https://doi.org/10.1007/978-981-99-7022-3_10