# GreedyViG: Dynamic Axial Graph Construction for Efficient Vision GNNs

arXiv: https://arxiv.org/abs/2405.06849 · HTML: https://mlsanity.com/paper/2405.06849/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 41 of its 51 references that are in the map.
- First posted on arXiv 10 May 2024.

## Builds on (most cited first, 30 of 41)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Training data-efficient image transformers & distillation through attention](https://mlsanity.com/paper/2012.12877/) (2020)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [A ConvNet for the 2020s](https://mlsanity.com/paper/2201.03545/) (2022)
- [Dynamic Graph CNN for Learning on Point Clouds](https://mlsanity.com/paper/1801.07829/) (2018)
- [Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions](https://mlsanity.com/paper/2102.12122/) (2021)
- [Graph Neural Networks: A Review of Methods and Applications](https://mlsanity.com/paper/1812.08434/) (2018)
- [MLP-Mixer: An all-MLP Architecture for Vision](https://mlsanity.com/paper/2105.01601/) (2021)
- [Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition](https://mlsanity.com/paper/1801.07455/) (2018)
- [EfficientNetV2: Smaller Models and Faster Training](https://mlsanity.com/paper/2104.00298/) (2021)
- [Designing Network Design Spaces](https://mlsanity.com/paper/2003.13678/) (2020)
- [Panoptic Feature Pyramid Networks](https://mlsanity.com/paper/1901.02446/) (2019)
- [MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer](https://mlsanity.com/paper/2110.02178/) (2021)
- [DeepGCNs: Can GCNs Go as Deep as CNNs?](https://mlsanity.com/paper/1904.03751/) (2019)
- [MetaFormer Is Actually What You Need for Vision](https://mlsanity.com/paper/2111.11418/) (2021)
- [LeViT: a Vision Transformer in ConvNet's Clothing for Faster Inference](https://mlsanity.com/paper/2104.01136/) (2021)
- [Conditional Positional Encodings for Vision Transformers](https://mlsanity.com/paper/2102.10882/) (2021)
- [Large-scale Point Cloud Semantic Segmentation with Superpoint Graphs](https://mlsanity.com/paper/1711.09869/) (2017)

## Built on by (most cited first, 21 of 21)

- [Hypergraph Vision Transformers: Images are More than Nodes, More than Edges](https://mlsanity.com/paper/2504.08710/) (2025)
- [WiGNet: Windowed Vision Graph Neural Network](https://mlsanity.com/paper/2410.00807/) (2024)
- [ClusterViG: Efficient Globally Aware Vision GNNs via Image Partitioning](https://mlsanity.com/paper/2501.10640/) (2025)
- [VCMamba: Bridging Convolutions with Multi-Directional Mamba for Efficient Visual Representation](https://mlsanity.com/paper/2509.04669/) (2025)
- [RapidNet: Multi-Level Dilated Convolution Based Mobile Backbone](https://mlsanity.com/paper/2412.10995/) (2024)
- [Vision Graph Prompting via Semantic Low-Rank Decomposition](https://mlsanity.com/paper/2505.04121/) (2025)
- [Explaining Vision GNNs: A Semantic and Visual Analysis of Graph-based Image Classification](https://mlsanity.com/paper/2504.19682/) (2025)
- [STRNet: Visual Navigation with Spatio-Temporal Representation through Dynamic Graph Aggregation](https://mlsanity.com/paper/2604.02829/) (2026)
- [AttentionViG: Cross-Attention-Based Dynamic Neighbor Aggregation in Vision GNNs](https://mlsanity.com/paper/2509.25570/) (2025)
- [Multi-Scale High-Resolution Logarithmic Grapher Module for Efficient Vision GNNs](https://mlsanity.com/paper/2510.13740/) (2025)
- [Domain-invariant Representation Learning via Segment Anything Model for Blood Cell Classification](https://mlsanity.com/paper/2408.07467/) (2024)
- [i-WiViG: Interpretable Window Vision GNN](https://mlsanity.com/paper/2503.08321/) (2025)
- [TGraphX: Tensor-Aware Graph Neural Network for Multi-Dimensional Feature Learning](https://mlsanity.com/paper/2504.03953/) (2025)
- [Cognitively-Inspired Emergent Communication via Knowledge Graphs for Assisting the Visually Impaired](https://mlsanity.com/paper/2505.22087/) (2025)
- [ViG-LRGC: Vision Graph Neural Networks with Learnable Reparameterized Graph Construction](https://mlsanity.com/paper/2509.18840/) (2025)
- [Through the PRISm: Importance-Aware Scene Graphs for Image Retrieval](https://mlsanity.com/paper/2512.18407/) (2025)
- [Patch as Node: Human-Centric Graph Representation Learning for Multimodal Action Recognition](https://mlsanity.com/paper/2512.21916/) (2025)
- [Attention Is not Everything: Efficient Alternatives for Vision](https://mlsanity.com/paper/2604.17439/) (2026)
- [GraphLeap: Decoupling Graph Construction and Convolution for Vision GNN Acceleration on FPGA](https://mlsanity.com/paper/2604.21290/) (2026)
- [Structured Hyperedge Adaptation for Parameter-Efficient Fine-Tuning of Vision Transformers](https://mlsanity.com/paper/2606.22383/) (2026)
- [StegGNN: Learning Graphical Representation for Image Steganography](https://mlsanity.com/paper/2609.32362/) (2026)

## Abstract (arXiv)

Vision graph neural networks (ViG) offer a new avenue for exploration in computer vision. A major bottleneck in ViGs is the inefficient k-nearest neighbor (KNN) operation used for graph construction. To solve this issue, we propose a new method for designing ViGs, Dynamic Axial Graph Construction (DAGC), which is more efficient than KNN as it limits the number of considered graph connections made within an image. Additionally, we propose a novel CNN-GNN architecture, GreedyViG, which uses DAGC. Extensive experiments show that GreedyViG beats existing ViG, CNN, and ViT architectures in terms of accuracy, GMACs, and parameters on image classification, object detection, instance segmentation, and semantic segmentation tasks. Our smallest model, GreedyViG-S, achieves 81.1% top-1 accuracy on ImageNet-1K, 2.9% higher than Vision GNN and 2.2% higher than Vision HyperGraph Neural Network (ViHGNN), with less GMACs and a similar number of parameters. Our largest model, GreedyViG-B obtains 83.9% top-1 accuracy, 0.2% higher than Vision GNN, with a 66.6% decrease in parameters and a 69% decrease in GMACs. GreedyViG-B also obtains the same accuracy as ViHGNN with a 67.3% decrease in parameters and a 71.3% decrease in GMACs. Our work shows that hybrid CNN-GNN architectures not only provide a new avenue for designing efficient models, but that they can also exceed the performance of current state-of-the-art models.