Towards Stable Cross-Domain Depression Recognition under Missing Modalities

• URL: http://arxiv.org/abs/2512.06447v1
• Date: Sat, 06 Dec 2025 14:19:57 GMT
• Title: Towards Stable Cross-Domain Depression Recognition under Missing Modalities
• Authors: Jiuyi Chen, Mingkui Tan, Haifeng Lu, Qiuna Xu, Zhihua Wang, Runhao Zeng, Xiping Hu,
• Abstract summary: Depression poses serious public health risks, including suicide, underscoring the urgency of timely and scalable screening.<n>We propose a unified framework for Stable Cross-Domain Depression Recognition based on Multimodal Large Language Model (SCD-MLLM)<n>The framework supports the integration and processing of heterogeneous depression-related data collected from varied sources.
• Score: 46.292478012586066
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Depression poses serious public health risks, including suicide, underscoring the urgency of timely and scalable screening. Multimodal automatic depression detection (ADD) offers a promising solution; however, widely studied audio- and video-based ADD methods lack a unified, generalizable framework for diverse depression recognition scenarios and show limited stability to missing modalities, which are common in real-world data. In this work, we propose a unified framework for Stable Cross-Domain Depression Recognition based on Multimodal Large Language Model (SCD-MLLM). The framework supports the integration and processing of heterogeneous depression-related data collected from varied sources while maintaining stability in the presence of incomplete modality inputs. Specifically, SCD-MLLM introduces two key components: (i) Multi-Source Data Input Adapter (MDIA), which employs masking mechanism and task-specific prompts to transform heterogeneous depression-related inputs into uniform token sequences, addressing inconsistency across diverse data sources; (ii) Modality-Aware Adaptive Fusion Module (MAFM), which adaptively integrates audio and visual features via a shared projection mechanism, enhancing resilience under missing modality conditions. e conduct comprehensive experiments under multi-dataset joint training settings on five publicly available and heterogeneous depression datasets from diverse scenarios: CMDC, AVEC2014, DAIC-WOZ, DVlog, and EATD. Across both complete and partial modality settings, SCD-MLLM outperforms state-of-the-art (SOTA) models as well as leading commercial LLMs (Gemini and GPT), demonstrating superior cross-domain generalization, enhanced ability to capture multimodal cues of depression, and strong stability to missing modality cases in real-world applications.

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