# DynMultiDep: A Dynamic Multimodal Fusion and Multi-Scale Time Series Modeling Approach for Depression Detection

## TL;DR

This paper introduces DynMultiDep, a new framework for detecting depression using dynamic multimodal data analysis that improves accuracy by better modeling time series and adapting to data characteristics.

## Contribution

The paper introduces a dynamic multimodal fusion framework with multi-scale temporal modeling for depression detection.

## Key findings

- DynMultiDep outperforms existing methods on two large-scale depression datasets.
- The framework effectively captures both long-term trends and short-term fluctuations in multimodal data.
- Dynamic fusion strategies improve adaptability to modality complementarity and redundancy.

## Abstract

Depression is a prevalent mental disorder that imposes a significant public health burden worldwide. Although multimodal detection methods have shown potential, existing techniques still face two critical bottlenecks: (i) insufficient integration of global patterns and local fluctuations in long-sequence modeling and (ii) static fusion strategies that fail to dynamically adapt to the complementarity and redundancy among modalities. To address these challenges, this paper proposes a dynamic multimodal depression detection framework, DynMultiDep, which combines multi-scale temporal modeling with an adaptive fusion mechanism. The core innovations of DynMultiDep lie in its Multi-scale Temporal Experts Module (MTEM) and Dynamic Multimodal Fusion module (DynMM). On one hand, MTEM employs Mamba experts to extract long-term trend features and utilizes local-window Transformers to capture short-term dynamic fluctuations, achieving adaptive fusion through a long-short routing mechanism. On the other hand, DynMM introduces modality-level and fusion-level dynamic decision-making, selecting critical modality paths and optimizing cross-modal interaction strategies based on input characteristics. The experimental results demonstrate that DynMultiDep outperforms existing state-of-the-art methods in detection performance on two widely used large-scale depression datasets.