Timezone: »

LatentGNN: Learning Efficient Non-local Relations for Visual Recognition
Songyang Zhang · Xuming He · Shipeng Yan

Thu Jun 13 05:05 PM -- 05:10 PM (PDT) @ Hall A

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.

Author Information

Songyang Zhang (ShanghaiTech University)
Xuming He (ShanghaiTech University)
Shipeng Yan (ShanghaiTech University)

Related Events (a corresponding poster, oral, or spotlight)