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.

该研究提出了一种基于图神经网络和潜在空间的方法，以降低图的复杂度从而实现对视觉特征的非局部关联表示，并在三项主要的视觉识别任务上展示了其具有较高的性能和较低的计算成本。