论文标题
多视图自动构造图卷积网络具有自适应类加权损失的语义分段
Multi-view Self-Constructing Graph Convolutional Networks with Adaptive Class Weighting Loss for Semantic Segmentation
论文作者
论文摘要
我们为语义分割提出了一种新颖的架构，称为多视图自我结构图卷积网络（MSCG-NET）。建立在最近提出的自我构造图（SCG）模块的基础上，该模块利用可学习的潜在变量直接从输入功能直接从输入功能中进行自构建基础图，而无需依赖手动构建的先验知识图，我们利用多个视图来显式地利用空中空运图像中的旋转不一使。我们进一步发展了自适应级别的加权损失，以解决班级失衡。我们证明了所提出的方法对农业视觉挑战数据集的有效性和灵活性，与基于纯CNN相关的工作相比，参数少得多，计算成本较低，并且计算成本较低。代码将在以下网址提供：github.com/samleoqh/mscg-net
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
