Depression is a serious mental disorder affecting millions of people all over the world.
Traditional clinical diagnosis methods are subjective, complicated and require extensive …

Given the prevalence of depression worldwide and its major impact on society, several
studies employed artificial intelligence modelling to automatically detect and assess …

Failure to timely diagnose and effectively treat depression leads to over 280 million people
suffering from this psychological disorder worldwide. The information cues of depression …

Automatic depression diagnosis is a challenging problem, that requires integrating spatial-
temporal information and extracting features from audio-visual signals. In terms of privacy …

We propose a novel multitask learning attention-based deep neural network model, which
facilitates the fusion of various modalities. In particular, we use this network to both regress …

Depression detection research has increased over the last few decades, one major
bottleneck of which is the limited data availability and representation learning. Recently, self …

Cross-language, cross-cultural emotion recognition and accurate prediction of affective
disorders are two of the major challenges in affective computing today. In this work, we …

With the recent rise of# MeToo, an increasing number of personal stories about sexual
harassment and sexual abuse have been shared online. In order to push forward the fight …

Depression is a prevalent and debilitating mental health condition that requires accurate
and efficient detection for timely and effective treatment. In this study, we utilized the E-DAIC …

Depression is one of the most common mental health disorders and can lead to suicide in
extreme cases. It is crucial to develop and design an automatic depression detection (ADD) …