Dynamic Graph Message Passing Networks

Understand

Modelling long-range dependencies is critical for scene understanding tasks in computer vision.

• Although CNNs have excelled in many vision tasks, they are still limited in capturing long-range structured relationships as they typically consist of layers of local kernels.
• A fully-connected graph is beneficial for such modelling, however, its computational overhead is prohibitive.
• We propose a dynamic graph message passing network, that significantly reduces the computational complexity compared to related works modelling a fully-connected graph.