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We propose a novel architecture called the Multi-view Self-Constructing GraphConvolutional Networks (MSCG-Net) for semantic segmentation. Building on therecently proposed Self-Constructing Graph (SCG) module, which makes use oflearnable latent variables to self-construct the underlying graphs directlyfrom the input features without relying on manually built prior knowledgegraphs, we leverage multiple views in order to explicitly exploit therotational invariance in airborne images. We further develop an adaptive classweighting loss to address the class imbalance. We demonstrate the effectivenessand flexibility of the proposed method on the Agriculture-Vision challengedataset and our model achieves very competitive results (0.547 mIoU) with muchfewer parameters and at a lower computational cost compared to related pure-CNNbased work. Code will be available at: github.com/samleoqh/MSCG-Net

Multi-view Self-Constructing Graph Convolutional Networks with Adaptive Class Weighting Loss for Semantic Segmentation