  Depression can significantly impact many aspects of an individual's life,
including their personal and social functioning, academic and work performance,
and overall quality of life. Many researchers within the field of affective
computing are adopting deep learning technology to explore potential patterns
related to the detection of depression. However, because of subjects' privacy
protection concerns, that data in this area is still scarce, presenting a
challenge for the deep discriminative models used in detecting depression. To
navigate these obstacles, a large-scale multimodal vlog dataset (LMVD), for
depression recognition in the wild is built. In LMVD, which has 1823 samples
with 214 hours of the 1475 participants captured from four multimedia platforms
(Sina Weibo, Bilibili, Tiktok, and YouTube). A novel architecture termed
MDDformer to learn the non-verbal behaviors of individuals is proposed.
Extensive validations are performed on the LMVD dataset, demonstrating superior
performance for depression detection. We anticipate that the LMVD will
contribute a valuable function to the depression detection community. The data
and code will released at the link: https://github.com/helang818/LMVD/.
