论文标题
FaceChannels：AffWild 2挑战的序列罢工
The FaceChannelS: Strike of the Sequences for the AffWild 2 Challenge
论文作者
论文摘要
在过去几年中，从人脸上预测的情感信息成为大多数机器学习社区的一项流行任务。众多标记的数据集的可用性支持了巨大和密集的深神经网络的发展。在大多数情况下，这些模型呈现为最新的基准，但很难适应其他情况。在本文中，我们介绍了一个基准测试FaceChannel神经网络的不同版本的一章：我们演示了我们的小模型如何从新颖的AffWild2数据集中的面部表达中预测情感信息。
Predicting affective information from human faces became a popular task for most of the machine learning community in the past years. The development of immense and dense deep neural networks was backed by the availability of numerous labeled datasets. These models, most of the time, present state-of-the-art results in such benchmarks, but are very difficult to adapt to other scenarios. In this paper, we present one more chapter of benchmarking different versions of the FaceChannel neural network: we demonstrate how our little model can predict affective information from the facial expression on the novel AffWild2 dataset.
