  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.
