Data inconsistency and bias are inevitable among different facial expression
recognition (FER) datasets due to subjective annotating process and different
collecting conditions. Recent works resort to adversarial mechanisms that learn
domain-invariant features to mitigate domain shift. However, most of these
works focus on holistic feature adaptation, and they ignore local features that
are more transferable across different datasets. Moreover, local features carry
more detailed and discriminative content for expression recognition, and thus
integrating local features may enable fine-grained adaptation. In this work, we
propose a novel Adversarial Graph Representation Adaptation (AGRA) framework
that unifies graph representation propagation with adversarial learning for
cross-domain holistic-local feature co-adaptation. To achieve this, we first
build a graph to correlate holistic and local regions within each domain and
another graph to correlate these regions across different domains. Then, we
learn the per-class statistical distribution of each domain and extract
holistic-local features from the input image to initialize the corresponding
graph nodes. Finally, we introduce two stacked graph convolution networks to
propagate holistic-local feature within each domain to explore their
interaction and across different domains for holistic-local feature
co-adaptation. In this way, the AGRA framework can adaptively learn
fine-grained domain-invariant features and thus facilitate cross-domain
expression recognition. We conduct extensive and fair experiments on several
popular benchmarks and show that the proposed AGRA framework achieves superior
performance over previous state-of-the-art methods.

本文提出了一种名为 AGRA 的新颖的敌对图表示适应框架，将图表示传播与敌对学习结合起来，实现跨域整体 - 局部特征共适应，并探索它们的相互作用，使 AGRA 框架能够自适应地学习细粒度的不变特征，从而促进跨域表情识别。