Adaptive Global-Local Representation Learning and Selection for
  Cross-Domain Facial Expression Recognition
Yuefang Gao Yuhao Xie Zeke Zexi Hu Tianshui Chen Liang Lin
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.