LatentGNN: Learning Efficient Non-local Relations for Visual Recognition

Songyang Zhang; Shipeng Yan; Xuming He

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arXiv · 1905.11634

LatentGNN: Learning Efficient Non-local Relations for Visual Recognition

Songyang Zhang·Shipeng Yan·Xuming He

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Abstract

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.

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BibTeX RIS

Songyang Zhang, Shipeng Yan, Xuming He. 2019-05-28. LatentGNN: Learning Efficient Non-local Relations for Visual Recognition. https://arxiv.org/abs/1905.11634

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