基于人格引导的公私域解耦超图-Former网络的多模态抑郁症检测
Personality-guided Public-Private Domain Disentangled Hypergraph-Former Network for Multimodal Depression Detection
AI总结：
针对多模态抑郁症检测中个体差异与跨模态时间依赖建模的挑战，提出P³HF网络，通过人格引导表示学习、超图-Former架构及公私域解耦，在MPDD-Young数据集上分类性能提升约10%，并验证了各组件有效性。
摘要：
抑郁症是一项全球性心理健康挑战，需要高效可靠的自动化检测方法。当前基于Transformer或图神经网络（GNN）的多模态抑郁症检测方法，在不同行为场景下对个体差异建模及跨模态时间依赖关系建模方面面临重大挑战。为此，我们提出P³HF（人格引导的公私域解耦超图-Former网络），其三大创新点为：（1）基于大语言模型（LLM）的人格引导表示学习，将离散个体特征转化为上下文描述以实现个性化编码；（2）超图-Former架构，用于建模高阶跨模态时间关系；（3）事件级公私域解耦与对比学习，以提升不同行为场景下的泛化能力。在MPDD-Young数据集上的实验显示，P³HF在二分类和三分类抑郁症检测任务中，准确率和加权F1值较现有方法提升约10%。大量消融实验验证了各架构组件的独立贡献，证实人格引导表示学习和高阶超图推理对生成鲁棒、个体感知的抑郁症相关表示均至关重要。代码已发布于https://github.com/hacilab/P3HF。
英文摘要：
Depression represents a global mental health challenge requiring efficient and reliable automated detection methods. Current Transformer- or Graph Neural Networks (GNNs)-based multimodal depression detection methods face significant challenges in modeling individual differences and cross-modal temporal dependencies across diverse behavioral contexts. Therefore, we propose P$^3$HF (Personality-guided Public-Private Domain Disentangled Hypergraph-Former Network) with three key innovations: (1) personality-guided representation learning using LLMs to transform discrete individual features into contextual descriptions for personalized encoding; (2) Hypergraph-Former architecture modeling high-order cross-modal temporal relationships; (3) event-level domain disentanglement with contrastive learning for improved generalization across behavioral contexts. Experiments on MPDD-Young dataset show P$^3$HF achieves around 10\% improvement on accuracy and weighted F1 for binary and ternary depression classification task over existing methods. Extensive ablation studies validate the independent contribution of each architectural component, confirming that personality-guided representation learning and high-order hypergraph reasoning are both essential for generating robust, individual-aware depression-related representations. The code is released at https://github.com/hacilab/P3HF.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
