青岛大学学报（自然科学版）2023，Vol.36Issue(4)：18-25,34,9.DOI:10.3969/j.issn.1006-1037.2023.04.04
基于上下文感知空间坐标嵌入的时空图卷积网络
Spatio-temporal Graph Convolutional Networks with Context-aware Spatial Coordinate Embedding
摘要
Abstract
For the complex non-Euclidean structure of space,graph convolutional network is not easy to construct the input graph through Euclidean distance,a context-aware spatial coordinate embeddingSpatio-Temporal Graph Convolutional Network(STE-STA)model was proposed,which explicitly combines spa-tial context and correlation into the model,and based on geospatial auxiliary task learning,semantic spa-tial embedding and dynamic graph spatio-temporal attention gesture recognition.Firstly,a fully connected graph was constructed from the hand skeleton,and the node features and edges were automatically learned by learning the context-aware vector encoding of geographic coordinates and the self-attention mechanism.Then,the spatial autocorrelation in the data was predicted in parallel with the main task.The experimen-tal results show that on the DHG-14/28 dataset,the recognition rate of the proposed algorithm reaches 92.40%and 87.85%,which are higher than the current optimal model.On the SHREC'17 dataset,it is 0.60%and 0.10%higher than Spatio-Temporal Graph Convolutional Network(ST-GCN).关键词
语义空间嵌入/时空注意力/时空掩码Key words
semantic spatial embedding/temporal and spatial attention/space-time mask分类
信息技术与安全科学引用本文复制引用
杨超,丁文文,邓淦森..基于上下文感知空间坐标嵌入的时空图卷积网络[J].青岛大学学报（自然科学版）,2023,36(4):18-25,34,9.基金项目
国家自然科学基金(批准号:62171342)资助 （批准号:62171342）
安徽省自然科学基金(批准号:1908085MF186)资助. （批准号:1908085MF186）
