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：面部动作单元是情感计算领域的重要概念，动作单元检测一直是研究热点。现有方法因在稀缺的动作单元标注数据集上使用大量可学习参数，或过度依赖大量额外相关数据，存在过拟合问题。参数高效迁移学习为解决这些挑战提供了有前景的范式，但其现有方法缺乏针对动作单元特性的设计。为此，我们创新性地将参数高效迁移学习范式应用于动作单元检测，提出AUFormer及一种新颖的混合知识专家协作机制。针对特定动作单元的独立混合知识专家首先以最少的可学习参数整合个性化多尺度与关联知识，随后该专家与专家组内其他专家协作获取聚合信息，并将其注入冻结的视觉Transformer中，实现参数高效的动作单元检测。此外，我们设计了边际截断难度感知加权非对称损失函数，该函数能促使模型更关注激活的动作单元，区分未激活动作单元的识别难度，并剔除潜在误标注样本。通过域内、跨域、数据效率及微表情领域等多视角实验，证明AUFormer在不依赖额外相关数据的情况下具备最先进的性能与鲁棒的泛化能力。AUFormer代码公开于https://github.com/yuankaishen2001/AUFormer。