Face images contain a wide variety of attribute information. In this paper,
we propose a generalized framework for joint estimation of ordinal and nominal
attributes based on information sharing. We tackle the correlation problem
between heterogeneous attributes using hard parameter sharing of shallow
features, and trade-off multiple loss functions by considering homoskedastic
uncertainty for each attribute estimation task. This leads to optimal
estimation of multiple attributes of the face and reduces the training cost of
multitask learning. Experimental results on benchmarks with multiple face
attributes show that the proposed approach has superior performance compared to
state of the art. Finally, we discuss the bias issues arising from the proposed
approach in face attribute estimation and validate its feasibility on edge
systems.

基于信息共享，提出了一种广义框架来联合估计面部的序数和名义属性，并通过考虑每个属性估计任务的同方差不确定性来权衡多个损失函数，以实现面部多属性的最优估计，并降低多任务学习的训练成本，与现有方法相比，实验证明所提出的方法具有优越的性能，最后，讨论了面部属性估计的偏差问题，并验证其在边缘系统上的可行性。

利用不确定性加权的多任务学习用于异质面部属性估计

Multi-Task Learning Using Uncertainty to Weigh Losses for Heterogeneous  Face Attribute Estimation

In recent years, vision transformers have been introduced into face
recognition and analysis and have achieved performance breakthroughs. However,
most previous methods generally train a single model or an ensemble of models
to perform the desired task, which ignores the synergy among different tasks
and fails to achieve improved prediction accuracy, increased data efficiency,
and reduced training time. This paper presents a multi-purpose algorithm for
simultaneous face recognition, facial expression recognition, age estimation,
and face attribute estimation (40 attributes including gender) based on a
single Swin Transformer. Our design, the SwinFace, consists of a single shared
backbone together with a subnet for each set of related tasks. To address the
conflicts among multiple tasks and meet the different demands of tasks, a
Multi-Level Channel Attention (MLCA) module is integrated into each
task-specific analysis subnet, which can adaptively select the features from
optimal levels and channels to perform the desired tasks. Extensive experiments
show that the proposed model has a better understanding of the face and
achieves excellent performance for all tasks. Especially, it achieves 90.97%
accuracy on RAF-DB and 0.22 $\epsilon$-error on CLAP2015, which are
state-of-the-art results on facial expression recognition and age estimation
respectively. The code and models will be made publicly available at
this https URL

该研究提出了一种基于单一的 Swin Transformer 的多功能算法，用于同时进行人脸识别、面部表情识别、年龄估计和面部属性估计，并通过多层级通道注意力模块适应性地选择最佳级别和通道上的特征，以提高预测准确性、数据效率和训练速度。

SwinFace: 一种用于人脸识别、表情识别、年龄估计和属性估计的多任务 Transformer

SwinFace: A Multi-task Transformer for Face Recognition, Expression  Recognition, Age Estimation and Attribute Estimation

Face attribute estimation has many potential applications in video
surveillance, face retrieval, and social media. While a number of methods have
been proposed for face attribute estimation, most of them did not explicitly
consider the attribute correlation and heterogeneity (e.g., ordinal vs. nominal
and holistic vs. local) during feature representation learning. In this paper,
we present a Deep Multi-Task Learning (DMTL) approach to jointly estimate
multiple heterogeneous attributes from a single face image. In DMTL, we tackle
attribute correlation and heterogeneity with convolutional neural networks
(CNNs) consisting of shared feature learning for all the attributes, and
category-specific feature learning for heterogeneous attributes. We also
introduce an unconstrained face database (LFW+), an extension of public-domain
LFW, with heterogeneous demographic attributes (age, gender, and race) obtained
via crowdsourcing. Experimental results on benchmarks with multiple face
attributes (MORPH II, LFW+, CelebA, LFWA, and FotW) show that the proposed
approach has superior performance compared to state of the art. Finally,
evaluations on a public-domain face database (LAP) with a single attribute show
that the proposed approach has excellent generalization ability.

本文章提出了一种基于深度多任务学习的方法，使用卷积神经网络来估计单张人脸图像中的多个异质属性，并在 MORPH II、LFW + 等数据库上进行了实验，取得了优秀的性能。