General Facial Representation Learning in a Visual-Linguistic Manner

• URL: http://arxiv.org/abs/2112.03109v1
• Date: Mon, 6 Dec 2021 15:22:05 GMT
• Title: General Facial Representation Learning in a Visual-Linguistic Manner
• Authors: Yinglin Zheng, Hao Yang, Ting Zhang, Jianmin Bao, Dongdong Chen, Yangyu Huang, Lu Yuan, Dong Chen, Ming Zeng, Fang Wen
• Abstract summary: We introduce a framework, called FaRL, for general Facial Representation Learning in a visual-linguistic manner. We show that FaRL achieves better transfer performance compared with previous pre-trained models. Our model surpasses the state-of-the-art methods on face analysis tasks including face parsing and face alignment.
• Score: 45.92447707178299
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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