The predominant approach to facial action unit (AU) detection revolves around
a supervised multi-label binary classification problem. Existing methodologies
often encode pixel-level information of AUs, thereby imposing substantial
demands on model complexity and expressiveness. Moreover, this practice
elevates the susceptibility to overfitting due to the presence of noisy AU
labels. In the present study, we introduce a contrastive learning framework
enhanced by both supervised and self-supervised signals. The objective is to
acquire discriminative features, deviating from the conventional pixel-level
learning paradigm within the domain of AU detection. To address the challenge
posed by noisy AU labels, we augment the supervised signal through the
introduction of a self-supervised signal. This augmentation is achieved through
positive sample sampling, encompassing three distinct types of positive sample
pairs. Furthermore, to mitigate the imbalanced distribution of each AU type, we
employ an importance re-weighting strategy tailored for minority AUs. The
resulting loss, denoted as AUNCE, is proposed to encapsulate this strategy. Our
experimental assessments, conducted on two widely-utilized benchmark datasets
(BP4D and DISFA), underscore the superior performance of our approach compared
to state-of-the-art methods in the realm of AU detection.

通过引入一种增强的对比学习框架并结合监督和自监督信号，以获取具有区分性的特征，本研究通过样本采样、重加权策略以及损失函数 AUNCE 等方法，针对面部运动单元（AU）检测中困扰的像素级学习问题、标签噪音和 AU 类型不平衡等挑战，取得了优于最先进方法的优秀性能。

学习对比特征表示用于面部动作单元检测

Learning Contrastive Feature Representations for Facial Action Unit  Detection

Due to the prevalence of scale variance in nature images, we propose to use
image scale as a self-supervised signal for Masked Image Modeling (MIM). Our
method involves selecting random patches from the input image and downsampling
them to a low-resolution format. Our framework utilizes the latest advances in
super-resolution (SR) to design the prediction head, which reconstructs the
input from low-resolution clues and other patches. After 400 epochs of
pre-training, our Super Resolution Masked Autoencoders (SRMAE) get an accuracy
of 82.1% on the ImageNet-1K task. Image scale signal also allows our SRMAE to
capture scale invariance representation. For the very low resolution (VLR)
recognition task, our model achieves the best performance, surpassing DeriveNet
by 1.3%. Our method also achieves an accuracy of 74.84% on the task of
recognizing low-resolution facial expressions, surpassing the current
state-of-the-art FMD by 9.48%.

基于图像尺度的自监督信号，使用超分辨率方法设计的预测头部能够重建输入图像，提高图像识别任务的准确率并捕捉尺度不变表达。