This document discusses graph-based global reasoning networks for computer vision tasks. It proposes a graph convolutional network that operates on a fully-connected graph to capture global dependencies between any pair of nodes. The network learns a shared representation for each node by propagating information along the edges of the graph.

1. Graph-Based Global
Reasoning Networks
Yunpeng Chen, Marcus Rohrbach , Zhicheng Yan , Shuicheng Yan, Jiashi
Feng , Yannis Kalantidis
Pre by Guangrui Li

2. Motivation

3. • Y = AXW
• W denotes parameters of the convolution kernels d*d
• A is the adjacent Matrix n*n
• X is the node feature n*d
• Torch.enisum(‘xnn,xnd,dd->xnd’)