  In facial action unit (AU) recognition tasks, regional feature learning and
AU relation modeling are two effective aspects which are worth exploring.
However, the limited representation capacity of regional features makes it
difficult for relation models to embed AU relationship knowledge. In this
paper, we propose a novel multi-level adaptive ROI and graph learning (MARGL)
framework to tackle this problem. Specifically, an adaptive ROI learning module
is designed to automatically adjust the location and size of the predefined AU
regions. Meanwhile, besides relationship between AUs, there exists strong
relevance between regional features across multiple levels of the backbone
network as level-wise features focus on different aspects of representation. In
order to incorporate the intra-level AU relation and inter-level AU regional
relevance simultaneously, a multi-level AU relation graph is constructed and
graph convolution is performed to further enhance AU regional features of each
level. Experiments on BP4D and DISFA demonstrate the proposed MARGL
significantly outperforms the previous state-of-the-art methods.
