A Masked Self-Supervised Pretraining Method for Face Parsing

Zhuang Li, Leilei Cao, Hongbin Wang and Lihong Xu
Additional contact information
Zhuang Li: Department of Control Science and Engineering, Tongji University, Shanghai 201804, China
Leilei Cao: Ant Group, Hangzhou 310013, China
Hongbin Wang: Ant Group, Hangzhou 310013, China
Lihong Xu: Department of Control Science and Engineering, Tongji University, Shanghai 201804, China

Mathematics, 2022, vol. 10, issue 12, 1-13

Abstract: Face Parsing aims to partition the face into different semantic parts, which can be applied into many downstream tasks, e.g., face mask up, face swapping, and face animation. With the popularity of cameras, it is easier to acquire facial images. However, pixel-wise manually labeling is time-consuming and labor-intensive, which motivates us to explore the unlabeled data. In this paper, we present a self-supervised learning method attempting to make full use of the unlabeled facial images for face parsing. In particular, we randomly mask some patches in the central area of facial images, and the model is required to reconstruct the masked patches. This self-supervised pretraining is capable of making the model capture facial feature representations through these unlabeled data. After self-supervised pretraining, the model is fine-tuned on a few labeled data for the face parsing task. Experimental results show that the model achieves better performance for face parsing assisted by the self-supervised pretraining, which greatly decreases the labeling cost. Our approach achieves 74.41 mIoU on the LaPa test set fine-tuned on only 0.2% of the labeled data of the whole training data, surpassing the model that is directly trained by a large margin of +5.02 mIoU. In addition, our approach achieves a new state-of-the-art on the LaPa and CelebAMask-HQ test set.

Keywords: face parsing; semantic segmentation; self-supervised learning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/10/12/2002/pdf (application/pdf)
https://www.mdpi.com/2227-7390/10/12/2002/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:10:y:2022:i:12:p:2002-:d:835590

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().