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.

翻译：池化是计算机视觉中的关键操作，但骨架的特殊结构限制了现有池化策略在骨架图建模中的应用。本文提出一种改进型图池化网络（IGPN），主要创新包括：引入基于结构分区的区域感知池化策略，利用原始特征的相关矩阵自适应调整新生成特征中不同区域信息的权重，实现更灵活有效的处理。为防止判别信息不可逆丢失，提出交叉融合模块与信息补充模块，分别提供块级和输入级信息补充。作为即插即用结构，该操作可无缝集成现有基于GCN的模型。在多个挑战性基准上的大量评估表明，所提方案的有效性。例如，在NTU-RGB+D 60数据集跨主体评估中，IGPN在降低近70%计算量的同时比基线显著提升准确率；同时引入增强版进一步优化精度。