The intensity estimation of facial action units (AUs) is challenging due to
subtle changes in the person's facial appearance. Previous approaches mainly
rely on probabilistic models or predefined rules for modeling co-occurrence
relationships among AUs, leading to limited generalization. In contrast, we
present a new learning framework that automatically learns the latent
relationships of AUs via establishing semantic correspondences between feature
maps. In the heatmap regression-based network, feature maps preserve rich
semantic information associated with AU intensities and locations. Moreover,
the AU co-occurring pattern can be reflected by activating a set of feature
channels, where each channel encodes a specific visual pattern of AU. This
motivates us to model the correlation among feature channels, which implicitly
represents the co-occurrence relationship of AU intensity levels. Specifically,
we introduce a semantic correspondence convolution (SCC) module to dynamically
compute the correspondences from deep and low resolution feature maps, and thus
enhancing the discriminability of features. The experimental results
demonstrate the effectiveness and the superior performance of our method on two
benchmark datasets.

本论文提出了一种新的学习框架，通过建立特征映射之间的语义对应关系自动学习 AU 之间的潜在联系，从而实现面部动作单元的强度估计，其中语义对应卷积模块（SCC 模块）可以提高特征的可辨识性，并且在两个基准数据集上展现了显著的性能优越性。