Automatic Depression Detection via Learning and Fusing Features from Visual Cues

03/01/2022
by   Yanrong Guo, et al.
0

Depression is one of the most prevalent mental disorders, which seriously affects one's life. Traditional depression diagnostics commonly depends on rating with scales, which can be labor-intensive and subjective. In this context, Automatic Depression Detection (ADD) has been attracting more attention for its low cost and objectivity. ADD systems are able to detect depression automatically from some medical records, like video sequences. However, it remains challenging to effectively extract depression-specific information from long sequences, thereby hindering a satisfying accuracy. In this paper, we propose a novel ADD method via learning and fusing features from visual cues. Specifically, we firstly construct Temporal Dilated Convolutional Network (TDCN), in which multiple Dilated Convolution Blocks (DCB) are designed and stacked, to learn the long-range temporal information from sequences. Then, the Feature-Wise Attention (FWA) module is adopted to fuse different features extracted from TDCNs. The module learns to assign weights for the feature channels, aiming to better incorporate different kinds of visual features and further enhance the detection accuracy. Our method achieves the state-of-the-art performance on the DAIC_WOZ dataset compared to other visual-feature-based methods, showing its effectiveness.

READ FULL TEXT

page 1

page 2

page 3

research
03/30/2021

Temporal Memory Relation Network for Workflow Recognition from Surgical Video

Automatic surgical workflow recognition is a key component for developin...
research
07/19/2023

TUNeS: A Temporal U-Net with Self-Attention for Video-based Surgical Phase Recognition

To enable context-aware computer assistance in the operating room of the...
research
09/06/2022

Sequential Cross Attention Based Multi-task Learning

In multi-task learning (MTL) for visual scene understanding, it is cruci...
research
07/01/2020

Automatic Crack Detection on Road Pavements Using Encoder Decoder Architecture

Inspired by the development of deep learning in computer vision and obje...
research
07/09/2022

Dual-path Attention is All You Need for Audio-Visual Speech Extraction

Audio-visual target speech extraction, which aims to extract a certain s...
research
01/15/2023

ACTIVE: A Deep Model for Sperm and Impurity Detection in Microscopic Videos

The accurate detection of sperms and impurities is a very challenging ta...
research
06/30/2021

Cyclist Trajectory Forecasts by Incorporation of Multi-View Video Information

This article presents a novel approach to incorporate visual cues from v...

Please sign up or login with your details

Forgot password? Click here to reset