This paper presents a light-weight and accurate deep neural model for
audiovisual emotion recognition. To design this model, the authors followed a
philosophy of simplicity, drastically limiting the number of parameters to
learn from the target datasets, always choosing the simplest earning methods:
i) transfer learning and low-dimensional space embedding allows to reduce the
dimensionality of the representations. ii) The isual temporal information is
handled by a simple score-per-frame selection process, averaged across time.
iii) A simple frame selection echanism is also proposed to weight the images of
a sequence. iv) The fusion of the different modalities is performed at
prediction level (late usion). We also highlight the inherent challenges of the
AFEW dataset and the difficulty of model selection with as few as 383
validation equences. The proposed real-time emotion classifier achieved a
state-of-the-art accuracy of 60.64 % on the test set of AFEW, and ranked 4th at
he Emotion in the Wild 2018 challenge.

该研究提出了一种轻量级和准确的深度神经模型，通过迁移学习、降维和简单的帧选择机制实现音视频情感识别，取得了情感识别领域 State-of-the-art 的准确率。