We demonstrate that it is possible to perform face-related computer vision in the wild using
synthetic data alone. The community has long enjoyed the benefits of synthesizing training …

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 …

Due to the remarkable progress in recent years, deep face recognition is in great need of
public support for practical model production and further exploration. The demands are in …

The adaptation of a Generative Adversarial Network (GAN) aims to transfer a pre-trained
GAN to a target domain with limited training data. In this paper, we focus on the one-shot …

Face parsing infers a pixel-wise label to each facial component, which has drawn much
attention recently. Previous methods have shown their efficiency in face parsing, which …

This paper probes intrinsic factors behind typical failure cases (eg spatial inconsistency and
boundary confusion) produced by the existing state-of-the-art method in face parsing. To …

In recent years, vision transformers have been introduced into face recognition and analysis
and have achieved performance breakthroughs. However, most previous methods generally …

Capsule networks are designed to present the objects by a set of parts and their
relationships, which provide an insight into the procedure of visual perception. Although …

Face parsing aims to predict pixel-wise labels for facial components of a target face in an
image. Existing approaches usually crop the target face from the input image with respect to …

This paper presents a novel approach, called Prototype-based Self-Distillation (ProS), for
unsupervised face representation learning. The existing supervised methods heavily rely on …