General Facial Representation Learning in a Visual-Linguistic Manner
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.
PDF Abstract CVPR 2022 PDF CVPR 2022 Abstract
ImageNet
CelebA
LFW
300W
CelebAMask-HQ
LAION-400M
WFLW
LFWA
AFLW-19
LaPa
Results from the Paper
Ranked #1 on
Face Parsing
on CelebAMask-HQ
(using extra training data)
