用高斯滤波优化骨架图结构，提升模糊动作识别准确率
G3CN: Gaussian Topology Refinement Gated Graph Convolutional Network for Skeleton-Based Action Recognition
- 引入高斯滤波动态优化骨架拓扑图
- 结合GRU增强关键点间信息传递，提升模糊动作区分度
- 在多个基准上显著提升识别性能，适合动作识别研究者
图卷积网络（GCN）在基于骨架的动作识别中表现优异，主要得益于其利用图结构进行特征聚合的能力。然而，现有GCN在区分语义相近的模糊动作时仍存在局限，归因于对拓扑与空间特征的建模不足。为此，本文提出高斯拓扑精炼门控图卷积网络（G$^{3}$CN），通过高斯滤波器对骨架拓扑图进行重构，增强对模糊动作的表征能力；同时将门控循环单元（GRU）嵌入GCN框架，提升关键点间的特征传播效率。所提方法在多种GCN骨干网络上展现出良好泛化性。在NTU RGB+D、NTU RGB+D 120和NW-UCLA三个基准数据集上的大量实验表明，G$^{3}$CN能有效提升动作识别性能，尤其在处理模糊样本时优势显著。
原文摘要 · Abstract (English)
Graph Convolutional Networks (GCNs) have proven to be highly effective for skeleton-based action recognition, primarily due to their ability to leverage graph topology for feature aggregation, a key factor in extracting meaningful representations. However, despite their success, GCNs often struggle to effectively distinguish between ambiguous actions, revealing limitations in the representation of learned topological and spatial features. To address this challenge, we propose a novel approach, Gaussian Topology Refinement Gated Graph Convolution (G$^{3}$CN), to address the challenge of distinguishing ambiguous actions in skeleton-based action recognition. G$^{3}$CN incorporates a Gaussian filter to refine the skeleton topology graph, improving the representation of ambiguous actions. Additionally, Gated Recurrent Units (GRUs) are integrated into the GCN framework to enhance information propagation between skeleton points. Our method shows strong generalization across various GCN backbones. Extensive experiments on NTU RGB+D, NTU RGB+D 120, and NW-UCLA benchmarks demonstrate that G$^{3}$CN effectively improves action recognition, particularly for ambiguous samples.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。