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

arXiv: https://arxiv.org/abs/2004.10327 · HTML: https://mlsanity.com/paper/2004.10327/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Apr 2020.

## Built on by (most cited first, 10 of 10)

- [ChessMix: Spatial Context Data Augmentation for Remote Sensing Semantic Segmentation](https://mlsanity.com/paper/2108.11535/) (2021)
- [Common Practices and Taxonomy in Deep Multi-view Fusion for Remote Sensing Applications](https://mlsanity.com/paper/2301.01200/) (2022)
- [Multi-class Brain Tumor Segmentation using Graph Attention Network](https://mlsanity.com/paper/2302.05598/) (2023)
- [RSEA-MVGNN: Multi-View Graph Neural Network with Reliable Structural Enhancement and Aggregation](https://mlsanity.com/paper/2408.07331/) (2024)
- [Superpixels and Graph Convolutional Neural Networks for Efficient Detection of Nutrient Deficiency Stress from Aerial Imagery](https://mlsanity.com/paper/2104.10249/) (2021)
- [Is In-Domain Data Really Needed? A Pilot Study on Cross-Domain Calibration for Network Quantization](https://mlsanity.com/paper/2105.07331/) (2021)
- [Multi-modal land cover mapping of remote sensing images using pyramid attention and gated fusion networks](https://mlsanity.com/paper/2111.03845/) (2021)
- [Distribution-aware Interactive Attention Network and Large-scale Cloud Recognition Benchmark on FY-4A Satellite Image](https://mlsanity.com/paper/2401.03182/) (2024)
- [Agriculture-Vision Challenge 2024 -- The Runner-Up Solution for Agricultural Pattern Recognition via Class Balancing and Model Ensemble](https://mlsanity.com/paper/2406.12271/) (2024)
- [Automatic Uncertainty-Aware Synthetic Data Bootstrapping for Historical Map Segmentation](https://mlsanity.com/paper/2511.15875/) (2025)

## Abstract (arXiv)

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