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How to learn a universal facial representation that boosts all face analysistasks? This paper takes one step toward this goal. In this paper, we study thetransfer performance of pre-trained models on face analysis tasks and introducea framework, called FaRL, for general Facial Representation Learning in avisual-linguistic manner. On one hand, the framework involves a contrastiveloss to learn high-level semantic meaning from image-text pairs. On the otherhand, we propose exploring low-level information simultaneously to furtherenhance the face representation, by adding a masked image modeling. We performpre-training on LAION-FACE, a dataset containing large amount of faceimage-text pairs, and evaluate the representation capability on multipledownstream tasks. We show that FaRL achieves better transfer performancecompared with previous pre-trained models. We also verify its superiority inthe low-data regime. More importantly, our model surpasses the state-of-the-artmethods on face analysis tasks including face parsing and face alignment.

General Facial Representation Learning in a Visual-Linguistic Manner