Graph-Based Global Reasoning Networks

Understand

Globally modeling and reasoning over relations between regions can be beneficial for many computer vision tasks on both images and videos.

• Convolutional Neural Networks (CNNs) excel at modeling local relations by convolution operations, but they are typically inefficient at capturing global relations between distant regions and require stacking multiple convolution layers.
• In this work, we propose a new approach for reasoning globally in which a set of features are globally aggregated over the coordinate space and then projected to an interaction space where relational reasoning can be efficiently computed.
• After reasoning, relation-aware features are distributed back to the original coordinate space for down-stream tasks.