Prototype Memory is a powerful model for face representation learning. It
enables the training of face recognition models using datasets of any size,
with on-the-fly generation of prototypes (classifier weights) and efficient
ways of their utilization. Prototype Memory demonstrated strong results in many
face recognition benchmarks. However, the algorithm of prototype generation,
used in it, is prone to the problems of imperfectly calculated prototypes in
case of low-quality or poorly recognizable faces in the images, selected for
the prototype creation. All images of the same person, presented in the
mini-batch, used with equal weights, and the resulting averaged prototype could
be contaminated with imperfect embeddings of such face images. It can lead to
misdirected training signals and impair the performance of the trained face
recognition models. In this paper, we propose a simple and effective way to
improve Prototype Memory with quality-aware prototype generation. Quality-Aware
Prototype Memory uses different weights for images of different quality in the
process of prototype generation. With this improvement, prototypes get more
valuable information from high-quality images and less hurt by low-quality
ones. We propose and compare several methods of quality estimation and usage,
perform extensive experiments on the different face recognition benchmarks and
demonstrate the advantages of the proposed model compared to the basic version
of Prototype Memory.

通过质量感知的原型生成，我们提出了一种简单且有效的方式来改善原型记忆。通过对不同质量的图像赋予不同的权重，原型能从高质量图像中获取更有价值的信息，并减少受到低质量图像的影响，从而提升人脸识别模型的性能。

面部表征学习的质量感知原型记忆

Quality-Aware Prototype Memory for Face Representation Learning

This paper presents a novel approach, called Prototype-based
Self-Distillation (ProS), for unsupervised face representation learning. The
existing supervised methods heavily rely on a large amount of annotated
training facial data, which poses challenges in terms of data collection and
privacy concerns. To address these issues, we propose ProS, which leverages a
vast collection of unlabeled face images to learn a comprehensive facial
omni-representation. In particular, ProS consists of two vision-transformers
(teacher and student models) that are trained with different augmented images
(cropping, blurring, coloring, etc.). Besides, we build a face-aware retrieval
system along with augmentations to obtain the curated images comprising
predominantly facial areas. To enhance the discrimination of learned features,
we introduce a prototype-based matching loss that aligns the similarity
distributions between features (teacher or student) and a set of learnable
prototypes. After pre-training, the teacher vision transformer serves as a
backbone for downstream tasks, including attribute estimation, expression
recognition, and landmark alignment, achieved through simple fine-tuning with
additional layers. Extensive experiments demonstrate that our method achieves
state-of-the-art performance on various tasks, both in full and few-shot
settings. Furthermore, we investigate pre-training with synthetic face images,
and ProS exhibits promising performance in this scenario as well.

该论文提出了一种名为 ProS 的新方法，用于无监督的人脸表示学习，通过利用大量未标记的人脸图像进行综合的面部全表示学习，并通过原型匹配损失来增强学到特征的区分性，进一步针对不同任务进行微调，取得了领先的性能，同时还探讨了利用合成人脸图像进行预训练的效果。