# Representative Graph Neural Network

arXiv: https://arxiv.org/abs/2008.05202 · HTML: https://mlsanity.com/paper/2008.05202/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Aug 2020.

## Built on by (most cited first, 19 of 19)

- [Lite-HRNet: A Lightweight High-Resolution Network](https://mlsanity.com/paper/2104.06403/) (2021)
- [A Survey on Graph Neural Networks and Graph Transformers in Computer Vision: A Task-Oriented Perspective](https://mlsanity.com/paper/2209.13232/) (2022)
- [Capturing Omni-Range Context for Omnidirectional Segmentation](https://mlsanity.com/paper/2103.05687/) (2021)
- [PointFlow: Flowing Semantics Through Points for Aerial Image Segmentation](https://mlsanity.com/paper/2103.06564/) (2021)
- [Attention-guided Temporally Coherent Video Object Matting](https://mlsanity.com/paper/2105.11427/) (2021)
- [Graph Neural Networks Designed for Different Graph Types: A Survey](https://mlsanity.com/paper/2204.03080/) (2022)
- [A Unified Efficient Pyramid Transformer for Semantic Segmentation](https://mlsanity.com/paper/2107.14209/) (2021)
- [Deep Image Matting: A Comprehensive Survey](https://mlsanity.com/paper/2304.04672/) (2023)
- [Cascade Image Matting with Deformable Graph Refinement](https://mlsanity.com/paper/2105.02646/) (2021)
- [Dynamic Neural Representational Decoders for High-Resolution Semantic Segmentation](https://mlsanity.com/paper/2107.14428/) (2021)
- [Generic Perceptual Loss for Modeling Structured Output Dependencies](https://mlsanity.com/paper/2103.10571/) (2021)
- [DynST: Dynamic Sparse Training for Resource-Constrained Spatio-Temporal Forecasting](https://mlsanity.com/paper/2403.02914/) (2024)
- [The Heterophilic Snowflake Hypothesis: Training and Empowering GNNs for Heterophilic Graphs](https://mlsanity.com/paper/2406.12539/) (2024)
- [Semantic Segmentation via Pixel-to-Center Similarity Calculation](https://mlsanity.com/paper/2301.04870/) (2023)
- [CondNet: Conditional Classifier for Scene Segmentation](https://mlsanity.com/paper/2109.10322/) (2021)
- [Discourse-Aware Graph Networks for Textual Logical Reasoning](https://mlsanity.com/paper/2207.01450/) (2022)
- [Inferring Past Human Actions in Homes with Abductive Reasoning](https://mlsanity.com/paper/2210.13984/) (2022)
- [Dynamic Dual Sampling Module for Fine-Grained Semantic Segmentation](https://mlsanity.com/paper/2105.11657/) (2021)
- [Cross-Layer Attentive Feature Upsampling for Low-latency Semantic Segmentation](https://mlsanity.com/paper/2601.01167/) (2026)

## Abstract (arXiv)

Non-local operation is widely explored to model the long-range dependencies. However, the redundant computation in this operation leads to a prohibitive complexity. In this paper, we present a Representative Graph (RepGraph) layer to dynamically sample a few representative features, which dramatically reduces redundancy. Instead of propagating the messages from all positions, our RepGraph layer computes the response of one node merely with a few representative nodes. The locations of representative nodes come from a learned spatial offset matrix. The RepGraph layer is flexible to integrate into many visual architectures and combine with other operations. With the application of semantic segmentation, without any bells and whistles, our RepGraph network can compete or perform favourably against the state-of-the-art methods on three challenging benchmarks: ADE20K, Cityscapes, and PASCAL-Context datasets. In the task of object detection, our RepGraph layer can also improve the performance on the COCO dataset compared to the non-local operation. Code is available at https://git.io/RepGraph.