Personality-guided Public-Private Domain Disentangled Hypergraph-Former Network for Multimodal Depression Detection

• URL: http://arxiv.org/abs/2511.12460v1
• Date: Sun, 16 Nov 2025 05:14:37 GMT
• Title: Personality-guided Public-Private Domain Disentangled Hypergraph-Former Network for Multimodal Depression Detection
• Authors: Changzeng Fu, Shiwen Zhao, Yunze Zhang, Zhongquan Jian, Shiqi Zhao, Chaoran Liu,
• Abstract summary: Depression represents a global mental health challenge requiring efficient and reliable automated detection methods.<n>We propose P$3$HF (Personality-guided Public-Private Domain Disentangled Hypergraph-Former Network) with three key innovations.<n>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.
• Score: 11.865335030037519
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• MF-GCN: A Multi-Frequency Graph Convolutional Network for Tri-Modal Depression Detection Using Eye-Tracking, Facial, and Acoustic Features [2.957755315403663]
  Depression is a prevalent global mental health disorder characterised by persistent low mood and anhedonia.<n>We introduce a gold standard dataset of 103 clinically assessed participants collected through a tripartite data approach.<n>Eye tracking data quantifies the attentional bias towards negative stimuli that is frequently observed in depressed groups.<n>Audio and video data capture the affective flattening and psychomotor retardation characteristic of depression.
  arXiv  Detail & Related papers  (2025-11-19T18:18:53Z)
• DiffGRM: Diffusion-based Generative Recommendation Model [63.35379395455103]
  Generative recommendation (GR) is an emerging paradigm that represents each item via a tokenizer as an n-digit semantic ID (SID)<n>We propose DiffGRM, a diffusion-based GR model that replaces the autoregressive decoder with a masked discrete diffusion model (MDM)<n> Experiments show consistent gains over strong generative and discriminative recommendation baselines on multiple datasets.
  arXiv  Detail & Related papers  (2025-10-21T03:23:32Z)
• HIPPD: Brain-Inspired Hierarchical Information Processing for Personality Detection [15.590715592593535]
  Personality detection from text aims to infer an individual's personality traits based on linguistic patterns.<n>This paper presents HIPPD, a brain-inspired framework for personality detection that emulates the hierarchical information processing of the human brain.
  arXiv  Detail & Related papers  (2025-10-10T22:20:35Z)
• ProtoN: Prototype Node Graph Neural Network for Unconstrained Multi-Impression Ear Recognition [7.969162168078149]
  We propose a few-shot learning framework to process multiple impressions of an identity using a graph-based approach.<n>ProtoN achieves state-of-the-art performance, with Rank-1 identification accuracy of up to 99.60% and an Equal Error Rate (EER) as low as 0.025.
  arXiv  Detail & Related papers  (2025-08-06T12:21:38Z)
• Traits Run Deep: Enhancing Personality Assessment via Psychology-Guided LLM Representations and Multimodal Apparent Behaviors [46.55948528317124]
  We propose a novel personality assessment framework called textittextbfTraits Run Deep.<n>It employs textittextbfpsychology-informed prompts to elicit high-level personality-relevant semantic representations.<n>It devises a textittextbfText-Centric Trait Fusion Network that anchors rich text semantics to align and integrate asynchronous signals from other modalities.
  arXiv  Detail & Related papers  (2025-07-30T04:12:14Z)
• HAMLET-FFD: Hierarchical Adaptive Multi-modal Learning Embeddings Transformation for Face Forgery Detection [6.060036926093259]
  HAMLET-FFD is a cross-domain generalization framework for face forgery detection.<n>It integrates visual evidence with conceptual cues, emulating expert forensic analysis.<n>By design, HAMLET-FFD freezes all pretrained parameters, serving as an external plugin.
  arXiv  Detail & Related papers  (2025-07-28T15:09:52Z)
• Fair Deepfake Detectors Can Generalize [51.21167546843708]
  We show that controlling for confounders (data distribution and model capacity) enables improved generalization via fairness interventions.<n>Motivated by this insight, we propose Demographic Attribute-insensitive Intervention Detection (DAID), a plug-and-play framework composed of: i) Demographic-aware data rebalancing, which employs inverse-propensity weighting and subgroup-wise feature normalization to neutralize distributional biases; and ii) Demographic-agnostic feature aggregation, which uses a novel alignment loss to suppress sensitive-attribute signals.<n>DAID consistently achieves superior performance in both fairness and generalization compared to several state-of-the-art
  arXiv  Detail & Related papers  (2025-07-03T14:10:02Z)
• Counterfactual Intervention Feature Transfer for Visible-Infrared Person Re-identification [69.45543438974963]
  We find graph-based methods in the visible-infrared person re-identification task (VI-ReID) suffer from bad generalization because of two issues. The well-trained input features weaken the learning of graph topology, making it not generalized enough during the inference process. We propose a Counterfactual Intervention Feature Transfer (CIFT) method to tackle these problems.
  arXiv  Detail & Related papers  (2022-08-01T16:15:31Z)
• Dynamic Prototype Mask for Occluded Person Re-Identification [88.7782299372656]
  Existing methods mainly address this issue by employing body clues provided by an extra network to distinguish the visible part. We propose a novel Dynamic Prototype Mask (DPM) based on two self-evident prior knowledge. Under this condition, the occluded representation could be well aligned in a selected subspace spontaneously.
  arXiv  Detail & Related papers  (2022-07-19T03:31:13Z)
• Learning Multi-Granular Hypergraphs for Video-Based Person Re-Identification [110.52328716130022]
  Video-based person re-identification (re-ID) is an important research topic in computer vision. We propose a novel graph-based framework, namely Multi-Granular Hypergraph (MGH) to better representational capabilities. 90.0% top-1 accuracy on MARS is achieved using MGH, outperforming the state-of-the-arts schemes.
  arXiv  Detail & Related papers  (2021-04-30T11:20:02Z)
• Generalized Iris Presentation Attack Detection Algorithm under Cross-Database Settings [63.90855798947425]
  Presentation attacks pose major challenges to most of the biometric modalities. We propose a generalized deep learning-based presentation attack detection network, MVANet. It is inspired by the simplicity and success of hybrid algorithm or fusion of multiple detection networks.
  arXiv  Detail & Related papers  (2020-10-25T22:42:27Z)
• Multimodal Depression Severity Prediction from medical bio-markers using Machine Learning Tools and Technologies [0.0]
  Depression has been a leading cause of mental-health illnesses across the world. Using behavioural cues to automate depression diagnosis and stage prediction in recent years has relatively increased. The absence of labelled behavioural datasets and a vast amount of possible variations prove to be a major challenge in accomplishing the task.
  arXiv  Detail & Related papers  (2020-09-11T20:44:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.