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Domain shift poses a significant challenge in Cross-Domain Facial ExpressionRecognition (CD-FER) due to the distribution variation across differentdomains. Current works mainly focus on learning domain-invariant featuresthrough global feature adaptation, while neglecting the transferability oflocal features. Additionally, these methods lack discriminative supervisionduring training on target datasets, resulting in deteriorated featurerepresentation in target domain. To address these limitations, we propose anAdaptive Global-Local Representation Learning and Selection (AGLRLS) framework.The framework incorporates global-local adversarial adaptation andsemantic-aware pseudo label generation to enhance the learning ofdomain-invariant and discriminative feature during training. Meanwhile, aglobal-local prediction consistency learning is introduced to improveclassification results during inference. Specifically, the framework consistsof separate global-local adversarial learning modules that learndomain-invariant global and local features independently. We also design asemantic-aware pseudo label generation module, which computes semantic labelsbased on global and local features. Moreover, a novel dynamic thresholdstrategy is employed to learn the optimal thresholds by leveraging independentprediction of global and local features, ensuring filtering out the unreliablepseudo labels while retaining reliable ones. These labels are utilized formodel optimization through the adversarial learning process in an end-to-endmanner. During inference, a global-local prediction consistency module isdeveloped to automatically learn an optimal result from multiple predictions.We conduct comprehensive experiments and analysis based on a fair evaluationbenchmark. The results demonstrate that the proposed framework outperforms thecurrent competing methods by a substantial margin.

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