  Pooling is a crucial operation in computer vision, yet the unique structure
of skeletons hinders the application of existing pooling strategies to skeleton
graph modelling. In this paper, we propose an Improved Graph Pooling Network,
referred to as IGPN. The main innovations include: Our method incorporates a
region-awareness pooling strategy based on structural partitioning. The
correlation matrix of the original feature is used to adaptively adjust the
weight of information in different regions of the newly generated features,
resulting in more flexible and effective processing. To prevent the
irreversible loss of discriminative information, we propose a cross fusion
module and an information supplement module to provide block-level and
input-level information respectively. As a plug-and-play structure, the
proposed operation can be seamlessly combined with existing GCN-based models.
We conducted extensive evaluations on several challenging benchmarks, and the
experimental results indicate the effectiveness of our proposed solutions. For
example, in the cross-subject evaluation of the NTU-RGB+D 60 dataset, IGPN
achieves a significant improvement in accuracy compared to the baseline while
reducing Flops by nearly 70%; a heavier version has also been introduced to
further boost accuracy.
