  This paper presents our Facial Action Units (AUs) detection submission to the
fifth Affective Behavior Analysis in-the-wild Competition (ABAW). Our approach
consists of three main modules: (i) a pre-trained facial representation encoder
which produce a strong facial representation from each input face image in the
input sequence; (ii) an AU-specific feature generator that specifically learns
a set of AU features from each facial representation; and (iii) a
spatio-temporal graph learning module that constructs a spatio-temporal graph
representation. This graph representation describes AUs contained in all frames
and predicts the occurrence of each AU based on both the modeled spatial
information within the corresponding face and the learned temporal dynamics
among frames. The experimental results show that our approach outperformed the
baseline and the spatio-temporal graph representation learning allows our model
to generate the best results among all ablated systems. Our model ranks at the
4th place in the AU recognition track at the 5th ABAW Competition. Our code is
publicly available at https://github.com/wzh125/ABAW-5.
