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Depression Detection

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Showing 1–50 of 157 papers

TitleStatusHype
FG 2025 TrustFAA: the First Workshop on Towards Trustworthy Facial Affect Analysis: Advancing Insights of Fairness, Explainability, and Safety (TrustFAA)—0
Towards Machine Unlearning for Paralinguistic Speech Processing—0
Explainable Depression Detection using Masked Hard Instance Mining—0
Speech as a Multimodal Digital Phenotype for Multi-Task LLM-based Mental Health Prediction—0
Large Language Models for Depression Recognition in Spoken Language Integrating Psychological KnowledgeCode1
The First MPDD Challenge: Multimodal Personality-aware Depression Detection—0
Enhancing Depression Detection via Question-wise Modality FusionCode0
Why Pre-trained Models Fail: Feature Entanglement in Multi-modal Depression Detection—0
Cross-Subject Depression Level Classification Using EEG Signals with a Sample Confidence Method—0
Explainable Depression Detection in Clinical Interviews with Personalized Retrieval-Augmented Generation—0
SpeechT-RAG: Reliable Depression Detection in LLMs with Retrieval-Augmented Generation Using Speech Timing Information—0
Machine Learning Fairness for Depression Detection using EEG Data—0
Multimodal Magic Elevating Depression Detection with a Fusion of Text and Audio Intelligence—0
Towards Explainable Multimodal Depression Recognition for Clinical InterviewsCode0
U-Fair: Uncertainty-based Multimodal Multitask Learning for Fairer Depression Detection—0
Context-Aware Deep Learning for Multi Modal Depression DetectionCode1
EmoVerse: Exploring Multimodal Large Language Models for Sentiment and Emotion UnderstandingCode1
Depression detection from Social Media Bangla Text Using Recurrent Neural Networks—0
Synthetic Data Generation with LLM for Improved Depression Prediction—0
SAD-TIME: a Spatiotemporal-fused network for depression detection with Automated multi-scale Depth-wise and TIME-interval-related common feature extractor—0
Multi-aspect Depression Severity Assessment via Inductive Dialogue System—0
Systematic Review: Text Processing Algorithms in Machine Learning and Deep Learning for Mental Health Detection on Social Media—0
On-device Federated Learning in Smartphones for Detecting Depression from Reddit Posts—0
A Hybrid Graph Neural Network for Enhanced EEG-Based Depression Detection—0
Depression detection in social media posts using transformer-based models and auxiliary features—0
Probing mental health information in speech foundation models—0
DepMamba: Progressive Fusion Mamba for Multimodal Depression DetectionCode2
Advancing Depression Detection on Social Media Platforms Through Fine-Tuned Large Language Models—0
Language-Agnostic Analysis of Speech Depression Detection—0
Avengers Assemble: Amalgamation of Non-Semantic Features for Depression Detection—0
A BERT-Based Summarization approach for depression detection—0
Auto-Landmark: Acoustic Landmark Dataset and Open-Source Toolkit for Landmark Extraction—0
Sentiment Informed Sentence BERT-Ensemble Algorithm for Depression Detection—0
Deep Knowledge-Infusion For Explainable Depression Detection—0
Density Adaptive Attention-based Speech Network: Enhancing Feature Understanding for Mental Health Disorders—0
HiQuE: Hierarchical Question Embedding Network for Multimodal Depression DetectionCode1
Multimodal Gender Fairness in Depression Prediction: Insights on Data from the USA & China—0
FacialPulse: An Efficient RNN-based Depression Detection via Temporal Facial LandmarksCode1
NarrationDep: Narratives on Social Media For Automatic Depression Detection—0
They Look Like Each Other: Case-based Reasoning for Explainable Depression Detection on Twitter using Large Language Models—0
Heterogeneous Subgraph Network with Prompt Learning for Interpretable Depression Detection on Social Media—0
Depression Detection and Analysis using Large Language Models on Textual and Audio-Visual Modalities—0
A Depression Detection Method Based on Multi-Modal Feature Fusion Using Cross-Attention—0
Predicting Individual Depression Symptoms from Acoustic Features During Speech—0
We Care: Multimodal Depression Detection and Knowledge Infused Mental Health Therapeutic Response Generation—0
Underneath the Numbers: Quantitative and Qualitative Gender Fairness in LLMs for Depression Prediction—0
ComFeAT: Combination of Neural and Spectral Features for Improved Depression Detection—0
LMVD: A Large-Scale Multimodal Vlog Dataset for Depression Detection in the WildCode2
Multi-Explainable TemporalNet: An Interpretable Multimodal Approach using Temporal Convolutional Network for User-level Depression Detection—0
DAIC-WOZ: On the Validity of Using the Therapist's prompts in Automatic Depression Detection from Clinical InterviewsCode1
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