Dynamic facial expression recognition (DFER) is essential for understanding human emotions and behavior. However, conventional DFER methods, which primarily use dynamic facial data, often underutilize static expression images and their labels, limiting their performance and robustness. To overcome this, we introduce UniLearn, a novel unified learning paradigm that integrates static facial expression recognition (SFER) data to enhance DFER task. UniLearn employs a dual-modal self-supervised pre-training method, leveraging both facial expression images and videos to enhance a ViT model's spatiotemporal representation capability. Then, the pre-trained model is fine-tuned on both static and dynamic expression datasets using a joint fine-tuning strategy. To prevent negative transfer during joint fine-tuning, we introduce an innovative Mixture of Adapter Experts (MoAE) module that enables task-specific knowledge acquisition and effectively integrates information from both static and dynamic expression data. Extensive experiments demonstrate UniLearn's effectiveness in leveraging complementary information from static and dynamic facial data, leading to more accurate and robust DFER. UniLearn consistently achieves state-of-the-art performance on FERV39K, MAFW, and DFEW benchmarks, with weighted average recall (WAR) of 53.65\%, 58.44\%, and 76.68\%, respectively. The source code and model weights will be publicly available at \url{https://github.com/MSA-LMC/UniLearn}.

本研究解决了动态面部表情识别（DFER）中常规方法未能充分利用静态表情图像及其标签的问题。提出的UniLearn方法通过自监督预训练和联合微调整合静态与动态数据，显著提升了模型的时空表征能力。实验结果表明，UniLearn在多个基准上达到了最先进的性能，展现了静态和动态信息互补的潜力。

UniLearn：通过统一预训练和微调提升动态面部表情识别