  Facial action unit (AU) detection has long encountered the challenge of
detecting subtle feature differences when AUs activate. Existing methods often
rely on encoding pixel-level information of AUs, which not only encodes
additional redundant information but also leads to increased model complexity
and limited generalizability. Additionally, the accuracy of AU detection is
negatively impacted by the class imbalance issue of each AU type, and the
presence of noisy and false AU labels. In this paper, we introduce a novel
contrastive learning framework aimed for AU detection that incorporates both
self-supervised and supervised signals, thereby enhancing the learning of
discriminative features for accurate AU detection. To tackle the class
imbalance issue, we employ a negative sample re-weighting strategy that adjusts
the step size of updating parameters for minority and majority class samples.
Moreover, to address the challenges posed by noisy and false AU labels, we
employ a sampling technique that encompasses three distinct types of positive
sample pairs. This enables us to inject self-supervised signals into the
supervised signal, effectively mitigating the adverse effects of noisy labels.
Our experimental assessments, conducted on four widely-utilized benchmark
datasets (BP4D, DISFA, GFT and Aff-Wild2), underscore the superior performance
of our approach compared to state-of-the-art methods of AU detection. Our code
is available at \url{https://github.com/Ziqiao-Shang/AUNCE}.
