  Self-Rating Depression Scale (SDS) questionnaire has frequently been used for
efficient depression preliminary screening. However, the uncontrollable
self-administered measure can be easily affected by insouciantly or deceptively
answering, and producing the different results with the clinician-administered
Hamilton Depression Rating Scale (HDRS) and the final diagnosis. Clinically,
facial expression (FE) and actions play a vital role in clinician-administered
evaluation, while FE and action are underexplored for self-administered
evaluations. In this work, we collect a novel dataset of 200 subjects to
evidence the validity of self-rating questionnaires with their corresponding
question-wise video recording. To automatically interpret depression from the
SDS evaluation and the paired video, we propose an end-to-end hierarchical
framework for the long-term variable-length video, which is also conditioned on
the questionnaire results and the answering time. Specifically, we resort to a
hierarchical model which utilizes a 3D CNN for local temporal pattern
exploration and a redundancy-aware self-attention (RAS) scheme for
question-wise global feature aggregation. Targeting for the redundant long-term
FE video processing, our RAS is able to effectively exploit the correlations of
each video clip within a question set to emphasize the discriminative
information and eliminate the redundancy based on feature pair-wise affinity.
Then, the question-wise video feature is concatenated with the questionnaire
scores for final depression detection. Our thorough evaluations also show the
validity of fusing SDS evaluation and its video recording, and the superiority
of our framework to the conventional state-of-the-art temporal modeling
methods.
