Self-Constructing Graph Convolutional Networks for Semantic Labeling

• URL: http://arxiv.org/abs/2003.06932v2
• Date: Thu, 23 Apr 2020 13:44:08 GMT
• Title: Self-Constructing Graph Convolutional Networks for Semantic Labeling
• Authors: Qinghui Liu, Michael Kampffmeyer, Robert Jenssen, Arnt-B{\o}rre Salberg
• Abstract summary: We propose a novel architecture called the Self-Constructing Graph (SCG), which makes use of learnable latent variables to generate embeddings. SCG can automatically obtain optimized non-local context graphs from complex-shaped objects in aerial imagery. We demonstrate the effectiveness and flexibility of the proposed SCG on the publicly available ISPRS Vaihingen dataset.
• Score: 23.623276007011373
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Graph Neural Networks (GNNs) have received increasing attention in many fields. However, due to the lack of prior graphs, their use for semantic labeling has been limited. Here, we propose a novel architecture called the Self-Constructing Graph (SCG), which makes use of learnable latent variables to generate embeddings and to self-construct the underlying graphs directly from the input features without relying on manually built prior knowledge graphs. SCG can automatically obtain optimized non-local context graphs from complex-shaped objects in aerial imagery. We optimize SCG via an adaptive diagonal enhancement method and a variational lower bound that consists of a customized graph reconstruction term and a Kullback-Leibler divergence regularization term. We demonstrate the effectiveness and flexibility of the proposed SCG on the publicly available ISPRS Vaihingen dataset and our model SCG-Net achieves competitive results in terms of F1-score with much fewer parameters and at a lower computational cost compared to related pure-CNN based work. Our code will be made public soon.

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