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。