Facial Action Units (AU) is a vital concept in the realm of affective computing, and AU detection has always been a hot research topic. Existing methods suffer from overfitting issues due to the utilization of a large number of learnable parameters on scarce AU-annotated datasets or heavy reliance on substantial additional relevant data. Parameter-Efficient Transfer Learning (PETL) provides a promising paradigm to address these challenges, whereas its existing methods lack design for AU characteristics. Therefore, we innovatively investigate PETL paradigm to AU detection, introducing AUFormer and proposing a novel Mixture-of-Knowledge Expert (MoKE) collaboration mechanism. An individual MoKE specific to a certain AU with minimal learnable parameters first integrates personalized multi-scale and correlation knowledge. Then the MoKE collaborates with other MoKEs in the expert group to obtain aggregated information and inject it into the frozen Vision Transformer (ViT) to achieve parameter-efficient AU detection. Additionally, we design a Margin-truncated Difficulty-aware Weighted Asymmetric Loss (MDWA-Loss), which can encourage the model to focus more on activated AUs, differentiate the difficulty of unactivated AUs, and discard potential mislabeled samples. Extensive experiments from various perspectives, including within-domain, cross-domain, data efficiency, and micro-expression domain, demonstrate AUFormer's state-of-the-art performance and robust generalization abilities without relying on additional relevant data. The code for AUFormer is available at https://github.com/yuankaishen2001/AUFormer.

翻译：面部动作单元（AU）是情感计算领域的重要概念，AU检测一直是研究热点。现有方法因在稀缺的AU标注数据集上使用大量可学习参数而面临过拟合问题，或过度依赖大量额外的相关数据。参数高效迁移学习（PETL）为解决这些挑战提供了有前景的范式，但现有方法缺乏针对AU特性的专门设计。为此，我们创新性地将PETL范式引入AU检测，提出AUFormer及新型知识混合专家（MoKE）协作机制。每个特定AU的MoKE以最小可学习参数，首先整合个性化的多尺度及关联知识，随后与专家组中的其他MoKE协作获取聚合信息，并将其注入冻结的视觉Transformer（ViT），实现参数高效的AU检测。此外，我们设计了边缘截断难度感知加权非对称损失函数（MDWA-Loss），该损失能促使模型更关注激活态AU、区分非激活态AU的难易程度，并剔除潜在错误标注样本。在域内、跨域、数据效率及微表情域等多个视角的大量实验表明，AUFormer在不依赖额外相关数据的情况下具有最先进的性能和鲁棒的泛化能力。AUFormer代码开源于https://github.com/yuankaishen2001/AUFormer。