Prompt-Based Cross-Modal Feature Alignment for Weakly Supervised IFER

Infrared Facial Expression Recognition (IFER) encounters challenges in data acquisition and annotation under low-light conditions, making fully supervised training difficult. Although pre-trained Vision-Language Models (VLMs) can enhance generalization for downstream tasks, their insufficient attention modeling in cross-domain scenarios leads to ineffective local semantic correlation. To address this, we propose a Prompt-based Cross-modal feature Alignment (PCA) method that improves weakly supervised IFER performance by leveraging RGB facial expression data. The PCA framework comprises two key components: (1) a Cross-modal Prompt Transfer (CPT) strategy that integrates category-specific information to distinguish expressions, and (2) an Image-Guided Alignment (IGA) module that achieves feature alignment using dual-domain feature banks. Experimental results on two benchmark datasets demonstrate that our method significantly outperforms current state-of-the-art approaches, confirming its effectiveness and superiority.