Modeling Multimodal Depression Diagnosis From the Perspective of Local Depressive Representation

Depression recognition is critical for early detection and treatment. Existing works focus on modeling coarse-grained multimodal representation to estimate the depression level. However, these approaches often overlook the inherent locality of depressive representation, resulting in weak and sparse depressive frames being overlooked. In addition, they neglect the inter modal correlations and intra-modal patterns of mood change, limiting the learning of multimodal complementary information. Therefore, we present a Locality-Aware Multimodal Depression (LAMD) recognition model. Specifically, LAMD contains three innovations: 1) Considering the sparsity of depressive features, we propose an Adaptive Temporal Attention (ATA) module to adaptively highlight keyframes with depressive features and suppress irrelevant frames. Additionally, we introduce Segment Information Sharing (SIS) strategy to overcome the limitation of inter-segment independence, enabling global awareness of depressive features within the whole segment. 2) We revisit the audio-video multimodal interaction from the perspectives of inter-modal correlation and intra-modal smoothness, introducing frame-level multimodal attention consistency constraints and smooth constraints. Furthermore, we propose a local cross attention to enhance the inter-modal interactions in adjacent time. 3) Extensive experiments on several datasets demonstrate that LAMD achieves superior performance, with up to 7.21 RMSE and 76.77% F1-score on the AVEC2014 and NJAD dataset, outperforming the prior art by a notable 0.22% and 1.88% margin, respectively. Moreover, visual analysis reveals that LAMD can adaptively perceive depressive keyframes and focus on fine-grained facial regions known for capturing subtle depressive expressions.