This study introduces an innovative cross‐modal coattention network (CMCAN) framework, specifically designed to tackle the challenges of temporal misalignment and modality‐specific feature preservation in multimodal depression detection. The architecture comprises two fundamental components: (1) guided multihead attention, which facilitates context‐aware interactions across modalities, and (2) dedicated self‐attention pathways that ensure the preservation of key unimodal features. To enhance the estimation of depression severity, a cascaded fusion strategy is employed, combining feature superposition with hierarchical stacking. When evaluated on a Chinese localized depression dataset, CMCAN demonstrates exceptional performance, achieving optimal results (accuracy: 0.839; precision: 0.829; recall: 0.863) with its audiovisual guided coattention module (AVGA (SA (V), SA (A))) comprising three cascaded layers. The framework consistently surpasses unimodal baselines across various emotional valence stimuli (positive, neutral, and negative) and achieves state‐of‐the‐art performance on AVEC 2014 (MAE: 5.38). Comprehensive ablation studies validate the effectiveness of individual components, whereas comparative analyses demonstrate significant improvements over existing multimodal fusion approaches. These findings underscore the robustness and generalizability of CMCAN, validating its effectiveness in harmonizing cross‐modal synergy while preserving modality‐specific features, thereby advancing practical solutions for automated depression detection.

The labeling scheme is designed to label the XML nodes so that both ordered and un-ordered queries can be processed without accessing the original XML file. When XML data become dynamic, it is important to design a labeling scheme that can facilitate updates and support query processing efficiently. In this paper, we propose a novel containment labeling scheme called DXCL (Dynamic XML Containment Labeling) to effectively process updating in dynamic XML data. Compared with the existing dynamic labeling schemes, a distinguishing feature of DXCL is that DXCL is compact and efficient regardless of whether the documents are updated or not. DXCL uses fixed length integer numbers to label initial XML documents and hence yields compact label size and high query performance. When updates take place, DXCL also has high performance on both label updates and query processing especially in the case of skewed insertions. Experimental results conform the benefits of our approach over the previous dynamic schemes.