论文标题
人类表达识别的多模型合奏学习方法
Multi-model Ensemble Learning Method for Human Expression Recognition
论文作者
论文摘要
人类影响的分析在人类计算机相互作用（HCI）系统中起着至关重要的作用。由于难以捕获大量现实生活数据，因此当前的大多数方法主要集中在受控环境上，从而限制其应用程序方案。为了解决这个问题，我们根据集合学习方法提出解决方案。具体而言，我们将问题提出为分类任务，然后用不同类型的骨干网，有效网络和插入网训练几个表达式分类模型。之后，通过模型集合方法融合了几个模型的输出以预测最终结果。此外，我们介绍了多重集合方法，以训练和集合几个模型具有相同的体系结构，但数据分布不同，以增强解决方案的性能。我们对ABAW2022挑战的AFFWILD2数据集进行了许多实验，结果证明了解决方案的有效性。
Analysis of human affect plays a vital role in human-computer interaction (HCI) systems. Due to the difficulty in capturing large amounts of real-life data, most of the current methods have mainly focused on controlled environments, which limit their application scenarios. To tackle this problem, we propose our solution based on the ensemble learning method. Specifically, we formulate the problem as a classification task, and then train several expression classification models with different types of backbones--ResNet, EfficientNet and InceptionNet. After that, the outputs of several models are fused via model ensemble method to predict the final results. Moreover, we introduce the multi-fold ensemble method to train and ensemble several models with the same architecture but different data distributions to enhance the performance of our solution. We conduct many experiments on the AffWild2 dataset of the ABAW2022 Challenge, and the results demonstrate the effectiveness of our solution.
