As a mental disorder, depression has affected people's lives, works, and so on. Researchers have proposed various industrial intelligent systems in the pattern recognition field for audiovisual depression detection. This paper presents an end-to-end trainable intelligent system to generate high-level representations over the entire video clip. Specifically, a three-dimensional (3D) convolutional neural network equipped with a module spatiotemporal feature aggregation module (STFAM) is trained from scratch on audio/visual emotion challenge (AVEC)2013 and AVEC2014 data, which can model the discriminative patterns closely related to depression. In the STFAM, channel and spatial attention mechanism and an aggregation method, namely 3D DEP-NetVLAD, are integrated to learn the compact characteristic based on the feature maps. Extensive experiments on the two databases (i.e., AVEC2013 and AVEC2014) are illustrated that the proposed intelligent system can efficiently model the underlying depression patterns and obtain better performances over the most video-based depression recognition approaches. Case studies are presented to describes the applicability of the proposed intelligent system for industrial intelligence.

中文翻译：

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基于面部表情的抑郁量表智能评估系统及工业智能案例研究

抑郁症作为一种精神障碍，已经影响到人们的生活、工作等。研究人员在模式识别领域提出了各种用于视听抑郁症检测的工业智能系统。本文提出了一个端到端的可训练智能系统来生成整个视频剪辑的高级表示。具体来说，配备模块时空特征聚合模块（STFAM）的三维（3D）卷积神经网络在音频/视觉情感挑战（AVEC）2013 和 AVEC2014 数据上从头开始训练，可以对与沮丧。在 STFAM 中，通道和空间注意力机制以及聚合方法，即 3D DEP-NetVLAD，被集成以学习基于特征图的紧凑特征。对两个数据库（即 AVEC2013 和 AVEC2014）的大量实验表明，所提出的智能系统可以有效地模拟潜在的抑郁模式，并获得比大多数基于视频的抑郁识别方法更好的性能。案例研究介绍了所提出的工业智能智能系统的适用性。