  Capturing long-range dependencies in feature representations is crucial for
many visual recognition tasks. Despite recent successes of deep convolutional
networks, it remains challenging to model non-local context relations between
visual features. A promising strategy is to model the feature context by a
fully-connected graph neural network (GNN), which augments traditional
convolutional features with an estimated non-local context representation.
However, most GNN-based approaches require computing a dense graph affinity
matrix and hence have difficulty in scaling up to tackle complex real-world
visual problems. In this work, we propose an efficient and yet flexible
non-local relation representation based on a novel class of graph neural
networks. Our key idea is to introduce a latent space to reduce the complexity
of graph, which allows us to use a low-rank representation for the graph
affinity matrix and to achieve a linear complexity in computation. Extensive
experimental evaluations on three major visual recognition tasks show that our
method outperforms the prior works with a large margin while maintaining a low
computation cost.
