A Depression Detection Method Based on Multi-Modal Feature Fusion Using Cross-Attention

Shengjie Li; Y. L. Xiao; Su Hu

doi:10.1109/icaace65325.2025.11019096

ScienceGate Book Chapters

JOURNAL ARTICLE

A Depression Detection Method Based on Multi-Modal Feature Fusion Using Cross-Attention

Shengjie Li Y. L. Xiao Su Hu

Year: 2025 Pages: 1825-1831

DOI: 10.1109/icaace65325.2025.11019096

Get Full-Text PDF Get Analytical Report

Keywords:

Modal Computer science Feature (linguistics) Fusion Artificial intelligence Sensor fusion Depression (economics) Pattern recognition (psychology) Materials science

Metrics

Cited By

29.54

FWCI (Field Weighted Citation Impact)

Refs

0.98

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Citation History

Topics

Traditional Chinese Medicine Studies

Health Sciences → Medicine → Complementary and alternative medicine

Brain Tumor Detection and Classification

Life Sciences → Neuroscience → Neurology

Advanced Computing and Algorithms

Social Sciences → Social Sciences → Urban Studies

A Depression Detection Method Based on Multi-Modal Feature Fusion Using Cross-Attention

Metrics

Citation History

Topics

Related Documents

3D Multi-Modal Object Detection Based on Cross-Attention Feature Fusion

Cross-modal pedestrian detection algorithm based on attention feature fusion

An Automatic Depression Detection Method with Cross-Modal Fusion Network and Multi-head Attention Mechanism

Cross-modal multi-scale feature fusion-based RGB-T saliency object detection method

Multi-Feature Fusion Multi-Modal Sentiment Analysis Model Based on Cross-Attention Mechanism