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FC-AEN: Fully Convolutional Adaptive Ensemble Network for Automatic Depression Detection 期刊论文

编号：

24ECA7440982F992A3BBF1C352895C96
作者：

Wang, Yaowei#^[1,2]Lin, Zulong#^[2,3]Teng, Yan^[4];Cheng, Yuqi(程宇琪)^[5,6,7]Jiang, Hongyan(姜红燕)^[5]Yang, Yun*^[2,3]
语种：

英文
期刊：

IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS ISSN：2329-924X 2026 年 13 卷 2 期 (1381 - 1396) ; APR
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关键词：

Spatiotemporal phenomena Depression Computational modeling Computer architecture Adaptation models Videos Training Transformers Feature extraction Computational efficiency Automatic depression detection (ADD) full convolution multimodal ensemble
摘要：

Multimodal automatic depression detection (ADD) has garnered significant research interest due to its potential for providing fast, objective, and reliable assessments. Despite advancements in the field, several challenges persist. First, many existing methods employ fixed-size feature modeling, which fails to adapt to temporal variations in depressive emotional expression, resulting in the loss of critical depressive behavior information. Second, while attention-based detection methods effectively extract features, they incur substantial computational overhead. Finally, multimodal ensemble processes often encounter modality inertia and modality forgetting issues, which compromise model stability and performance. We propose an innovative multimodal ADD network called the fully convolutional adaptive ensemble network (FC-AEN) to address these challenges. This approach comprises three key modules. The first module, deformable patch embedding (DPE), dynamically adjusts the time range of feature segments during training to ensure comprehensive capture of depressive behaviors. The second module, fully convolutional spatiotemporal detector (FCSTD), enhances computational efficiency to manage larger data scales effectively. The third module, adaptive dynamic ensemble (ADE), addresses the multimodal unbalanced ensemble problem through alternating training and gradient adjustment. We conducted experiments on four datasets (AVEC2013, AVEC2014, AVEC2019, and CMDep) and achieved new state-of-the-art (SOTA) performance across three benchmarks. Specifically, on the AVEC2019 dataset, we achieved a mean absolute error (MAE) of 4.91, representing a relative reduction of 14.9%; on the AVEC2013 dataset, we achieved an MAE of 5.11 and a relative reduction of 5.0%; and on the CMDep dataset, we achieved an MAE of 6.04 and a relative reduction of 3.4%.
推荐引用方式
GB/T 7714：

Wang Yaowei,Lin Zulong,Teng Yan, et al. FC-AEN: Fully Convolutional Adaptive Ensemble Network for Automatic Depression Detection [J].IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS,2026,13(2):1381-1396.
APA：

Wang Yaowei,Lin Zulong,Teng Yan,Cheng Yuqi,&Yang Yun.(2026).FC-AEN: Fully Convolutional Adaptive Ensemble Network for Automatic Depression Detection .IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS,13(2):1381-1396.
MLA：

Wang Yaowei, et al. "FC-AEN: Fully Convolutional Adaptive Ensemble Network for Automatic Depression Detection" .IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS 13,2(2026):1381-1396.
入库时间：

2025/10/23 21:51:22
更新时间：

2026/1/11 22:56:55
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