To address the problem of data inconsistencies among different facial
expression recognition (FER) datasets, many cross-domain FER methods (CD-FERs)
have been extensively devised in recent years. Although each declares to
achieve superior performance, fair comparisons are lacking due to the
inconsistent choices of the source/target datasets and feature extractors. In
this work, we first analyze the performance effect caused by these inconsistent
choices, and then re-implement some well-performing CD-FER and recently
published domain adaptation algorithms. We ensure that all these algorithms
adopt the same source datasets and feature extractors for fair CD-FER
evaluations. We find that most of the current leading algorithms use
adversarial learning to learn holistic domain-invariant features to mitigate
domain shifts. However, these algorithms ignore local features, which are more
transferable across different datasets and carry more detailed content for
fine-grained adaptation. To address these issues, we integrate graph
representation propagation with adversarial learning for cross-domain
holistic-local feature co-adaptation by developing a novel adversarial graph
representation adaptation (AGRA) framework. Specifically, it first builds two
graphs to correlate holistic and local regions within each domain and across
different domains, respectively. Then, it extracts holistic-local features from
the input image and uses learnable per-class statistical distributions to
initialize the corresponding graph nodes. Finally, two stacked graph
convolution networks (GCNs) are adopted to propagate holistic-local features
within each domain to explore their interaction and across different domains
for holistic-local feature co-adaptation. We conduct extensive and fair
evaluations on several popular benchmarks and show that the proposed AGRA
framework outperforms previous state-of-the-art methods.

本文针对不同面部表情识别（FER）数据集间的数据不一致性问题，提出了一种新的跨域 FER 方法（AGRA），通过将图表示传播与对抗学习相结合，实现了域内和跨域间简化特征的自适应共适应。简单说，这个方法可以更好地识别面部表情并且比以前的方法表现更好。

跨域人脸表情识别：统一评估基准和对抗性图学习

Cross-Domain Facial Expression Recognition: A Unified Evaluation  Benchmark and Adversarial Graph Learning

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。