  Facial expression recognition (FER) is an important task in computer vision,
having practical applications in areas such as human-computer interaction,
education, healthcare, and online monitoring. In this challenging FER task,
there are three key issues especially prevalent: inter-class similarity,
intra-class discrepancy, and scale sensitivity. While existing works typically
address some of these issues, none have fully addressed all three challenges in
a unified framework. In this paper, we propose a two-stream Pyramid
crOss-fuSion TransformER network (POSTER), that aims to holistically solve all
three issues. Specifically, we design a transformer-based cross-fusion method
that enables effective collaboration of facial landmark features and image
features to maximize proper attention to salient facial regions. Furthermore,
POSTER employs a pyramid structure to promote scale invariance. Extensive
experimental results demonstrate that our POSTER achieves new state-of-the-art
results on RAF-DB (92.05%), FERPlus (91.62%), as well as AffectNet 7 class
(67.31%) and 8 class (63.34%). The code is available at
https://github.com/zczcwh/POSTER.
