# A Single Graph Convolution Is All You Need: Efficient Grayscale Image Classification

arXiv: https://arxiv.org/abs/2402.00564 · HTML: https://mlsanity.com/paper/2402.00564/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 30 references that are in the map.
- First posted on arXiv 1 Feb 2024.

## Builds on (most cited first, 21 of 21)

- [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)
- [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)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering](https://mlsanity.com/paper/1606.09375/) (2016)
- [MLP-Mixer: An all-MLP Architecture for Vision](https://mlsanity.com/paper/2105.01601/) (2021)
- [Residual Gated Graph ConvNets](https://mlsanity.com/paper/1711.07553/) (2017)
- [ResMLP: Feedforward networks for image classification with data-efficient training](https://mlsanity.com/paper/2105.03404/) (2021)
- [Vision GNN: An Image is Worth Graph of Nodes](https://mlsanity.com/paper/2206.00272/) (2022)
- [FastViT: A Fast Hybrid Vision Transformer using Structural Reparameterization](https://mlsanity.com/paper/2303.14189/) (2023)
- [Vision Transformer for Small-Size Datasets](https://mlsanity.com/paper/2112.13492/) (2021)
- [Topology Adaptive Graph Convolutional Networks](https://mlsanity.com/paper/1710.10370/) (2017)
- [Accurate, Low-latency, Efficient SAR Automatic Target Recognition on FPGA](https://mlsanity.com/paper/2301.01454/) (2023)
- [GraphAGILE: An FPGA-based Overlay Accelerator for Low-latency GNN Inference](https://mlsanity.com/paper/2302.01769/) (2023)
- [BA^2M: A Batch Aware Attention Module for Image Classification](https://mlsanity.com/paper/2103.15099/) (2021)
- [Mini-batch graphs for robust image classification](https://mlsanity.com/paper/2105.03237/) (2021)
- [Identity Connections in Residual Nets Improve Noise Stability](https://mlsanity.com/paper/1905.10944/) (2019)
- [Graph Neural Networks for Image Classification and Reinforcement Learning using Graph representations](https://mlsanity.com/paper/2203.03457/) (2022)

## Built on by (most cited first, 2 of 2)

- [A Unified Framework for Modeling Heterogeneous Financial Data via Dual-Granularity Prompting](https://mlsanity.com/paper/2404.13004/) (2024)
- [VTR: An Optimized Vision Transformer for SAR ATR Acceleration on FPGA](https://mlsanity.com/paper/2404.04527/) (2024)

## Abstract (arXiv)

Image classifiers for domain-specific tasks like Synthetic Aperture Radar Automatic Target Recognition (SAR ATR) and chest X-ray classification often rely on convolutional neural networks (CNNs). These networks, while powerful, experience high latency due to the number of operations they perform, which can be problematic in real-time applications. Many image classification models are designed to work with both RGB and grayscale datasets, but classifiers that operate solely on grayscale images are less common. Grayscale image classification has critical applications in fields such as medical imaging and SAR ATR. In response, we present a novel grayscale image classification approach using a vectorized view of images. By leveraging the lightweight nature of Multi-Layer Perceptrons (MLPs), we treat images as vectors, simplifying the problem to grayscale image classification. Our approach incorporates a single graph convolutional layer in a batch-wise manner, enhancing accuracy and reducing performance variance. Additionally, we develop a customized accelerator on FPGA for our model, incorporating several optimizations to improve performance. Experimental results on benchmark grayscale image datasets demonstrate the effectiveness of our approach, achieving significantly lower latency (up to 16× less on MSTAR) and competitive or superior performance compared to state-of-the-art models for SAR ATR and medical image classification.