# Class-wise Dynamic Graph Convolution for Semantic Segmentation

arXiv: https://arxiv.org/abs/2007.09690 · HTML: https://mlsanity.com/paper/2007.09690/

## Key facts

- Cited by 44 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jul 2020.

## Built on by (most cited first, 30 of 44)

- [Rethinking Semantic Segmentation: A Prototype View](https://mlsanity.com/paper/2203.15102/) (2022)
- [Large-scale Unsupervised Semantic Segmentation](https://mlsanity.com/paper/2106.03149/) (2021)
- [GMMSeg: Gaussian Mixture based Generative Semantic Segmentation Models](https://mlsanity.com/paper/2210.02025/) (2022)
- [SAM2-Adapter: Evaluating & Adapting Segment Anything 2 in Downstream Tasks: Camouflage, Shadow, Medical Image Segmentation, and More](https://mlsanity.com/paper/2408.04579/) (2024)
- [A Survey on Graph Neural Networks and Graph Transformers in Computer Vision: A Task-Oriented Perspective](https://mlsanity.com/paper/2209.13232/) (2022)
- [Learning Statistical Texture for Semantic Segmentation](https://mlsanity.com/paper/2103.04133/) (2021)
- [Structural and Statistical Texture Knowledge Distillation for Semantic Segmentation](https://mlsanity.com/paper/2305.03944/) (2023)
- [Learning Implicit Feature Alignment Function for Semantic Segmentation](https://mlsanity.com/paper/2206.08655/) (2022)
- [Heterogeneous Relational Complement for Vehicle Re-identification](https://mlsanity.com/paper/2109.07894/) (2021)
- [Learning Local and Global Temporal Contexts for Video Semantic Segmentation](https://mlsanity.com/paper/2204.03330/) (2022)
- [AerialFormer: Multi-resolution Transformer for Aerial Image Segmentation](https://mlsanity.com/paper/2306.06842/) (2023)
- [Ultra-High Resolution Segmentation with Ultra-Rich Context: A Novel Benchmark](https://mlsanity.com/paper/2305.10899/) (2023)
- [Tree-of-Table: Unleashing the Power of LLMs for Enhanced Large-Scale Table Understanding](https://mlsanity.com/paper/2411.08516/) (2024)
- [Self-Regulation for Semantic Segmentation](https://mlsanity.com/paper/2108.09702/) (2021)
- [LOGICSEG: Parsing Visual Semantics with Neural Logic Learning and Reasoning](https://mlsanity.com/paper/2309.13556/) (2023)
- [Guided Patch-Grouping Wavelet Transformer with Spatial Congruence for Ultra-High Resolution Segmentation](https://mlsanity.com/paper/2307.00711/) (2023)
- [Discrete Latent Perspective Learning for Segmentation and Detection](https://mlsanity.com/paper/2406.10475/) (2024)
- [A Unified Efficient Pyramid Transformer for Semantic Segmentation](https://mlsanity.com/paper/2107.14209/) (2021)
- [DCN-T: Dual Context Network with Transformer for Hyperspectral Image Classification](https://mlsanity.com/paper/2304.09915/) (2023)
- [PVG: Progressive Vision Graph for Vision Recognition](https://mlsanity.com/paper/2308.00574/) (2023)
- [View-Centric Multi-Object Tracking with Homographic Matching in Moving UAV](https://mlsanity.com/paper/2403.10830/) (2024)
- [PPTFormer: Pseudo Multi-Perspective Transformer for UAV Segmentation](https://mlsanity.com/paper/2406.19632/) (2024)
- [Structural and Statistical Texture Knowledge Distillation and Learning for Segmentation](https://mlsanity.com/paper/2503.08043/) (2025)
- [Tracing Hyperparameter Dependencies for Model Parsing via Learnable Graph Pooling Network](https://mlsanity.com/paper/2312.02224/) (2023)
- [VSR-Net: Vessel-like Structure Rehabilitation Network with Graph Clustering](https://mlsanity.com/paper/2312.13116/) (2023)
- [ChangeNet: Multi-Temporal Asymmetric Change Detection Dataset](https://mlsanity.com/paper/2312.17428/) (2023)
- [Specialize and Fuse: Pyramidal Output Representation for Semantic Segmentation](https://mlsanity.com/paper/2108.01866/) (2021)
- [SAM3-Adapter: Efficient Adaptation of Segment Anything 3 for Camouflage Object Segmentation, Shadow Detection, and Medical Image Segmentation](https://mlsanity.com/paper/2511.19425/) (2025)
- [Automatic Relation-aware Graph Network Proliferation](https://mlsanity.com/paper/2205.15678/) (2022)
- [Graph-Segmenter: Graph Transformer with Boundary-aware Attention for Semantic Segmentation](https://mlsanity.com/paper/2308.07592/) (2023)

## Abstract (arXiv)

Recent works have made great progress in semantic segmentation by exploiting contextual information in a local or global manner with dilated convolutions, pyramid pooling or self-attention mechanism. In order to avoid potential misleading contextual information aggregation in previous works, we propose a class-wise dynamic graph convolution (CDGC) module to adaptively propagate information. The graph reasoning is performed among pixels in the same class. Based on the proposed CDGC module, we further introduce the Class-wise Dynamic Graph Convolution Network(CDGCNet), which consists of two main parts including the CDGC module and a basic segmentation network, forming a coarse-to-fine paradigm. Specifically, the CDGC module takes the coarse segmentation result as class mask to extract node features for graph construction and performs dynamic graph convolutions on the constructed graph to learn the feature aggregation and weight allocation. Then the refined feature and the original feature are fused to get the final prediction. We conduct extensive experiments on three popular semantic segmentation benchmarks including Cityscapes, PASCAL VOC 2012 and COCO Stuff, and achieve state-of-the-art performance on all three benchmarks.