Dynamic Graph Neural Networks for Human Parsing

Graphs are prevalent in various real-world applications, and Graph Convolutional Networks (GCNs) have gained great popularity in tackling various analytics tasks on graph and network data. However, some recent studies raise concerns about whether GCNs can optimally integrate node features and topological structures in a complex graph with rich information. The conventional graph remains static, meaning that both the graph structure and the node features remain fixed over time. The adaptive graph structure allows the framework to effectively handle the evolving entities and relations, making it suitable for scenarios where the underlying data is subject to changes and updates over time.