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.

在该论文中，我们提出了一种改进的图池化网络（IGPN），包括区域感知池化策略、交叉融合模块和信息补充模块等创新点，该方法可以与现有的基于 GCN 的模型无缝结合，实验证明了我们提出的解决方案的有效性。

一种改进的基于骨架的动作识别图池化网络

An Improved Graph Pooling Network for Skeleton-Based Action Recognition

Human action recognition aims at classifying the category of human action
from a segment of a video. Recently, people have dived into designing GCN-based
models to extract features from skeletons for performing this task, because
skeleton representations are much more efficient and robust than other
modalities such as RGB frames. However, when employing the skeleton data, some
important clues like related items are also discarded. It results in some
ambiguous actions that are hard to be distinguished and tend to be
misclassified. To alleviate this problem, we propose an auxiliary feature
refinement head (FR Head), which consists of spatial-temporal decoupling and
contrastive feature refinement, to obtain discriminative representations of
skeletons. Ambiguous samples are dynamically discovered and calibrated in the
feature space. Furthermore, FR Head could be imposed on different stages of
GCNs to build a multi-level refinement for stronger supervision. Extensive
experiments are conducted on NTU RGB+D, NTU RGB+D 120, and NW-UCLA datasets.
Our proposed models obtain competitive results from state-of-the-art methods
and can help to discriminate those ambiguous samples. Codes are available at
this https URL

本文提出了一个辅助的特征细化头（FR Head），通过空间 - 时间分解和对比特征细化来识别骨架的歧义样本，从而强化对多层的监督，并在多个数据集上进行了广泛的实验，得到了与最先进方法相竞争的结果。