  The detection of facial action units (AUs) has been studied as it has the
competition due to the wide-ranging applications thereof. In this paper, we
propose a novel framework for the AU detection from a single input image by
grasping the \textbf{c}o-\textbf{o}ccurrence and \textbf{m}utual
\textbf{ex}clusion (COMEX) as well as the intensity distribution among AUs. Our
algorithm uses facial landmarks to detect the features of local AUs. The
features are input to a bidirectional long short-term memory (BiLSTM) layer for
learning the intensity distribution. Afterwards, the new AU feature
continuously passed through a self-attention encoding layer and a
continuous-state modern Hopfield layer for learning the COMEX relationships.
Our experiments on the challenging BP4D and DISFA benchmarks without any
external data or pre-trained models yield F1-scores of 63.7\% and 61.8\%
respectively, which shows our proposed networks can lead to performance
improvement in the AU detection task.
