  We propose a novel architecture called the Multi-view Self-Constructing Graph
Convolutional Networks (MSCG-Net) for semantic segmentation. Building on the
recently proposed Self-Constructing Graph (SCG) module, which makes use of
learnable latent variables to self-construct the underlying graphs directly
from the input features without relying on manually built prior knowledge
graphs, we leverage multiple views in order to explicitly exploit the
rotational invariance in airborne images. We further develop an adaptive class
weighting loss to address the class imbalance. We demonstrate the effectiveness
and flexibility of the proposed method on the Agriculture-Vision challenge
dataset and our model achieves very competitive results (0.547 mIoU) with much
fewer parameters and at a lower computational cost compared to related pure-CNN
based work. Code will be available at: github.com/samleoqh/MSCG-Net
