  Depression, a prominent contributor to global disability, affects a
substantial portion of the population. Efforts to detect depression from social
media texts have been prevalent, yet only a few works explored depression
detection from user-generated video content. In this work, we address this
research gap by proposing a simple and flexible multi-modal temporal model
capable of discerning non-verbal depression cues from diverse modalities in
noisy, real-world videos. We show that, for in-the-wild videos, using
additional high-level non-verbal cues is crucial to achieving good performance,
and we extracted and processed audio speech embeddings, face emotion
embeddings, face, body and hand landmarks, and gaze and blinking information.
Through extensive experiments, we show that our model achieves state-of-the-art
results on three key benchmark datasets for depression detection from video by
a substantial margin. Our code is publicly available on GitHub.
