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DepMamba: Progressive Fusion Mamba for Multimodal Depression Detection

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Multimodal Interpretable Depression Analysis Using Visual, Physiological, Audio and Textual Data

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Conformal Depression Prediction

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Cross-modal interaction and multi-source visual fusion for video generation in fetal cardiac screening

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Nonlinear Regularization Decoding Method for Speech Recognition

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A Novel Audio-Visual Multimodal Semi-Supervised Model Based on Graph Neural Networks for Depression Detection

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Automated Speech Analysis for Screening and Monitoring Bipolar Depression: Machine Learning Model Development and Interpretation Study

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ParaLBench: A Large-Scale Benchmark for Computational Paralinguistics Over Acoustic Foundation Models

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Cross-Modal Fusion Mamba for Multimodal Depression Detection

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Multimodal Time-Series Recognition With Spatio–Temporal Dynamic Graph Spike Neural Networks

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Multimodal Local Global Interaction Networks for Automatic Depression Severity Estimation

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Investigating Acoustic-Textual Emotional Inconsistency Information for Automatic Depression Detection

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FC-AEN: Fully Convolutional Adaptive Ensemble Network for Automatic Depression Detection

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A Symphony of Sentiments using Log-Melspectrogram Techniques for Emotional Classification

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Real-Time Feedback System for Stage Performance Using Multi-Modal Deep Learning Techniques

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LiteGuard: Efficient Offline Language Model for Resource-Constrained Healthcare

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DepFormer: A Unified Framework with Bimodal Collaborative Transformer for Depression Detection

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Modeling Multimodal Depression Diagnosis From the Perspective of Local Depressive Representation

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DSTC: A Multimodal Network for Depression Emotion Recognition and Sentiment Analysis

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FaceDepth: A Robust Unimodal Depression Detection Framework Using Invariant Facial Landmark Features

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TREAT: Facial Depression Recognition by Learning Joint Depression Score and Level Distribution

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Two-stage multiple instance learning networks with attention-based hybrid aggregation for speech emotion recognition

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Explainable visual emotion recognition via modular reasoning

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Large Language Models for Depression Detection: A Review with Prospects of Incomplete Multimodality

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Harmonizing Emotions: A Novel Approach to Audio Emotion Classification using Log-Melspectrogram with Augmentation

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PACMR: Progressive Adaptive Crossmodal Reinforcement for Multimodal Apparent Personality Traits Analysis

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Contrastive Learning Joint Regularization for Pathological Image Classification with Noisy Labels

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Emotional Resonance Unleashed by exploring Novel Audio Classification Techniques with Log- Melspectrogram Augmentation

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Achieving Enhanced Bi-Linear Attention Network for Teaching Manner Analysis Over Edge Cloud-Assisted AIoT: Voice-Body Coordination Perspective

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Fine-Tuning Large Language Models With Behavioral Alignment for Depression Detection

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Feature-based enhanced boosting algorithm for depression detection

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TRANSHEALTH: A Transformer-BDI Hybrid Framework for Real-Time Psychological Distress Detection in Ambient Healthcare

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Tabular Prior-Data Fitted Networks for Objective Depression Screening with Portable EEG and ECG (Preprint)Preprint

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ThyHisTer: A new thyroid histopathology image dataset for ternary classification of thyroid cancer

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MSMT-FN: Multi-segment Multi-task Fusion Network for Marketing Audio Classification

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MoDE: Improving Mixture of Depression Experts With Mutual Information Estimator for Depression Detection

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Spatio-temporal fusion of fNIRS signals with multi-view structured sparse canonical correlation analysis for depression detection

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Optimized adaptive depression state prediction and severity estimation from twitter data

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CDFN: Credibility-Driven Fusion Network for Multimodal Sentiment Analysis

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Advancing depression detection in audio through innovative semi-supervised learning technology

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