Domain shift poses a significant challenge in Cross-Domain Facial Expression
Recognition (CD-FER) due to the distribution variation across different
domains. Current works mainly focus on learning domain-invariant features
through global feature adaptation, while neglecting the transferability of
local features. Additionally, these methods lack discriminative supervision
during training on target datasets, resulting in deteriorated feature
representation in target domain. To address these limitations, we propose an
Adaptive Global-Local Representation Learning and Selection (AGLRLS) framework.
The framework incorporates global-local adversarial adaptation and
semantic-aware pseudo label generation to enhance the learning of
domain-invariant and discriminative feature during training. Meanwhile, a
global-local prediction consistency learning is introduced to improve
classification results during inference. Specifically, the framework consists
of separate global-local adversarial learning modules that learn
domain-invariant global and local features independently. We also design a
semantic-aware pseudo label generation module, which computes semantic labels
based on global and local features. Moreover, a novel dynamic threshold
strategy is employed to learn the optimal thresholds by leveraging independent
prediction of global and local features, ensuring filtering out the unreliable
pseudo labels while retaining reliable ones. These labels are utilized for
model optimization through the adversarial learning process in an end-to-end
manner. During inference, a global-local prediction consistency module is
developed to automatically learn an optimal result from multiple predictions.
We conduct comprehensive experiments and analysis based on a fair evaluation
benchmark. The results demonstrate that the proposed framework outperforms the
current competing methods by a substantial margin.

针对交叉领域面部表情识别中领域转移的挑战，提出一种自适应的全局 - 局部表示学习与选择框架，通过全局 - 局部对抗性适应和语义感知伪标签生成来增强训练中的领域不变性和判别特征学习，同时，在推断过程中引入了全局 - 局部预测一致性学习来改进分类结果，通过一系列实验，结果表明该框架在公平评估中比现有竞争方法表现出明显的优势。

自适应全局局部表征学习与选择 用于跨领域面部表情识别

Adaptive Global-Local Representation Learning and Selection for  Cross-Domain Facial Expression Recognition

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。