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

本文提出了一种名为 MSCG-Net 的新型架构，用于语义分割，在最新提出的 Self-Constructing Graph（SCG）模块上构建，通过利用多个视图来明确地利用空中图像的旋转不变性，进一步开发了一个自适应类加权损失来处理类别不平衡问题，证明了所提出方法的有效性和灵活性，与相关纯 CNN 比较，我们的模型在 Agriculture-Vision 挑战数据集上实现了非常有竞争力的结果（0.547 mIoU），并且具有更少的参数和更低的计算成本。