# Scaling Graph Convolutions for Mobile Vision

arXiv: https://arxiv.org/abs/2406.05850 · HTML: https://mlsanity.com/paper/2406.05850/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 34 references that are in the map.
- First posted on arXiv 9 Jun 2024.

## Builds on (most cited first, 26 of 26)

- [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)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features](https://mlsanity.com/paper/1905.04899/) (2019)
- [RandAugment: Practical automated data augmentation with a reduced search space](https://mlsanity.com/paper/1909.13719/) (2019)
- [Searching for MobileNetV3](https://mlsanity.com/paper/1905.02244/) (2019)
- [Random Erasing Data Augmentation](https://mlsanity.com/paper/1708.04896/) (2017)
- [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)
- [Conditional Positional Encodings for Vision Transformers](https://mlsanity.com/paper/2102.10882/) (2021)
- [EfficientFormer: Vision Transformers at MobileNet Speed](https://mlsanity.com/paper/2206.01191/) (2022)
- [Mobile-Former: Bridging MobileNet and Transformer](https://mlsanity.com/paper/2108.05895/) (2021)
- [Vision GNN: An Image is Worth Graph of Nodes](https://mlsanity.com/paper/2206.00272/) (2022)
- [Separable Self-attention for Mobile Vision Transformers](https://mlsanity.com/paper/2206.02680/) (2022)
- [FastViT: A Fast Hybrid Vision Transformer using Structural Reparameterization](https://mlsanity.com/paper/2303.14189/) (2023)
- [Rethinking Vision Transformers for MobileNet Size and Speed](https://mlsanity.com/paper/2212.08059/) (2022)
- [EdgeViTs: Competing Light-weight CNNs on Mobile Devices with Vision Transformers](https://mlsanity.com/paper/2205.03436/) (2022)
- [MobileViG: Graph-Based Sparse Attention for Mobile Vision Applications](https://mlsanity.com/paper/2307.00395/) (2023)

## Built on by (most cited first, 9 of 9)

- [iFormer: Integrating ConvNet and Transformer for Mobile Application](https://mlsanity.com/paper/2501.15369/) (2025)
- [ClusterViG: Efficient Globally Aware Vision GNNs via Image Partitioning](https://mlsanity.com/paper/2501.10640/) (2025)
- [CARE Transformer: Mobile-Friendly Linear Visual Transformer via Decoupled Dual Interaction](https://mlsanity.com/paper/2411.16170/) (2024)
- [RapidNet: Multi-Level Dilated Convolution Based Mobile Backbone](https://mlsanity.com/paper/2412.10995/) (2024)
- [AdaptViG: Adaptive Vision GNN with Exponential Decay Gating](https://mlsanity.com/paper/2511.09942/) (2025)
- [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)
- [ViG-LRGC: Vision Graph Neural Networks with Learnable Reparameterized Graph Construction](https://mlsanity.com/paper/2509.18840/) (2025)
- [GraphLeap: Decoupling Graph Construction and Convolution for Vision GNN Acceleration on FPGA](https://mlsanity.com/paper/2604.21290/) (2026)

## Abstract (arXiv)

To compete with existing mobile architectures, MobileViG introduces Sparse Vision Graph Attention (SVGA), a fast token-mixing operator based on the principles of GNNs. However, MobileViG scales poorly with model size, falling at most 1% behind models with similar latency. This paper introduces Mobile Graph Convolution (MGC), a new vision graph neural network (ViG) module that solves this scaling problem. Our proposed mobile vision architecture, MobileViGv2, uses MGC to demonstrate the effectiveness of our approach. MGC improves on SVGA by increasing graph sparsity and introducing conditional positional encodings to the graph operation. Our smallest model, MobileViGv2-Ti, achieves a 77.7% top-1 accuracy on ImageNet-1K, 2% higher than MobileViG-Ti, with 0.9 ms inference latency on the iPhone 13 Mini NPU. Our largest model, MobileViGv2-B, achieves an 83.4% top-1 accuracy, 0.8% higher than MobileViG-B, with 2.7 ms inference latency. Besides image classification, we show that MobileViGv2 generalizes well to other tasks. For object detection and instance segmentation on MS COCO 2017, MobileViGv2-M outperforms MobileViG-M by 1.2 APbox and 0.7 APmask, and MobileViGv2-B outperforms MobileViG-B by 1.0 APbox and 0.7 APmask. For semantic segmentation on ADE20K, MobileViGv2-M achieves 42.9% mIoU and MobileViGv2-B achieves 44.3% mIoU. Our code can be found at urlhttps://github.com/SLDGroup/MobileViGv2.