Fine-Grained Self-Paced Relational Preserving Network for Cross-Domain Few-Shot Facial Expression Recognition

Abstract

Cross-domain few-shot facial expression recognition (CF-FER) aims to adapt models trained on basic expressions to recognize novel compound expressions using only a few annotated examples. Although vision-language models (VLMs) have shown promise in few-shot learning, their application to CF-FER remains challenging due to two key issues: coarse-grained textual prompts that fail to capture subtle variations among compound expressions, and episodic training that tends to overfit on highly overlapping few-shot tasks. To address these issues, we propose a fine-grained self-paced relational preserving network (FSR-Net), which introduces fine-grained action unit (AU)-aware textual descriptions generated by large language models (LLMs) to enrich semantic representations and provide more discriminative prototypes. Based on this, we introduce a self-paced relational preserving regularization (SPR) strategy that leverages structural discrepancies between teacher-student visual features and textual-enhanced prototypes as reliability indicators. By progressively weighting reliable samples while filtering out harder ones, the regularization strategy explicitly preserves relational consistency across samples and mitigates overfitting in CF-FER. Comprehensive experiments on multiple CF-FER benchmarks confirm the effectiveness of FSR-Net, yielding average improvements of 5.78% (1-shot) and 3.80% (5-shot) over prior state-of-the-art methods. These results demonstrate its superior capacity for capturing subtle expression cues and enhancing cross-domain transferability.