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

arXiv:2403.00561v1 Announce Type: new
Abstract: 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 …

abstract arxiv correlation cs.ai cs.cv face framework generalized images information losses multi-task learning ordinal paper type uncertainty