General Facial Representation Learning in a Visual-Linguistic Manner - Zheng, Yinglin; Yang, Hao; Zhang, Ting; Bao, Jianmin; Chen, Dongdong; Huang, Yangyu; Yuan, Lu; Chen, Dong; Zeng, Ming; Wen, Fang (2022 Computer Vision and Pattern Recognition, 2022)
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.