Explainable Depression Assessment from Face Videos by Weakly Supervised Learning

Existing video-based automatic depression assessment (ADA) approaches frequently achieve video-level depression assessment by aggregating features or predictions of individual frames or equal-length segments within the given video. While their performances have been largely enhanced by recent advanced deep learning models, they typically fail to explicitly consider the varied importance of depression-related behavioural cues across different video segments, i.e., segments within one video may contain behaviours reflecting varying depression status. Underestimating segment-level variations can obscure the detection of facial behaviour cues associated with depression, thereby undermining the accuracy and interpretability of video-based depression detection systems. In this paper, we propose a novel video-based ADA approach that specifically identifies and differentiates video segments that exhibit depression-related facial behaviours across varying temporal durations, providing clear insights into how each segment contributes to the video-level depression prediction. To achieve this, a novel weakly supervised strategy is proposed to compare segment-level behaviours with video-level depression label, enabling the model to assign depression-relevant scores to multiple temporal scale video segments and attend selectively to those most indicative of depressive states. Extensive experiments on the AVEC 2013 and AVEC 2014 face video depression datasets demonstrate the effectiveness of our methods. Our code is provided in the Supplementary Material.