论文标题
BYEL：引导您的情绪潜在
BYEL : Bootstrap Your Emotion Latent
论文作者
论文摘要
随着深度学习的改善，试图将深度学习应用于人类情绪分析的研究数量正在迅速增加。但是，即使发生了这种趋势，仍然很难获得高质量的图像和注释。因此，从合成图像中学习的合成数据（LSD）挑战的学习是最有趣的领域之一。通常，域的适应方法被广泛用于应对LSD挑战，但仍有一个限制，即目标域（真实图像）仍然需要。为了关注这些局限性，我们提出了一个框架引导您的情绪潜伏（BYEL），该框架仅在训练中使用合成图像。 BYEL是通过将情感分类器和情感向量减法添加到BYOL框架中来实现的，该框架在自我监督的表示学习中表现良好。我们使用从AFF-WILD2数据集生成的合成图像训练我们的框架，并使用AFF-WILD2数据集中的真实图像对其进行评估。结果表明，我们的框架（0.3084）在宏F1评分公制上的框架（0.3084）比基线（0.3）高2.8％。
With the improved performance of deep learning, the number of studies trying to apply deep learning to human emotion analysis is increasing rapidly. But even with this trend going on, it is still difficult to obtain high-quality images and annotations. For this reason, the Learning from Synthetic Data (LSD) Challenge, which learns from synthetic images and infers from real images, is one of the most interesting areas. In general, Domain Adaptation methods are widely used to address LSD challenges, but there is a limitation that target domains (real images) are still needed. Focusing on these limitations, we propose a framework Bootstrap Your Emotion Latent (BYEL), which uses only synthetic images in training. BYEL is implemented by adding Emotion Classifiers and Emotion Vector Subtraction to the BYOL framework that performs well in Self-Supervised Representation Learning. We train our framework using synthetic images generated from the Aff-wild2 dataset and evaluate it using real images from the Aff-wild2 dataset. The result shows that our framework (0.3084) performs 2.8% higher than the baseline (0.3) on the macro F1 score metric.
