How to learn a universal facial representation that boosts all face analysis
tasks? This paper takes one step toward this goal. In this paper, we study the
transfer performance of pre-trained models on face analysis tasks and introduce
a framework, called FaRL, for general Facial Representation Learning in a
visual-linguistic manner. On one hand, the framework involves a contrastive
loss to learn high-level semantic meaning from image-text pairs. On the other
hand, we propose exploring low-level information simultaneously to further
enhance the face representation, by adding a masked image modeling. We perform
pre-training on LAION-FACE, a dataset containing large amount of face
image-text pairs, and evaluate the representation capability on multiple
downstream tasks. We show that FaRL achieves better transfer performance
compared with previous pre-trained models. We also verify its superiority in
the low-data regime. More importantly, our model surpasses the state-of-the-art
methods on face analysis tasks including face parsing and face alignment.

本研究提出了一种通用的人脸表示学习框架（FaRL），其使用对比损失和掩蔽图像建模来同时掌握高层语义和低层信息。在大量的图像和文本对上进行预训练并在多个下游任务中验证其性能，在人脸解析和人脸对齐等人脸分析任务中展现了比以往预训练模型更好的迁移表现。