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。