# Lightweight Vision Transformer with Window and Spatial Attention for Food Image Classification

arXiv: https://arxiv.org/abs/2509.18692 · HTML: https://mlsanity.com/paper/2509.18692/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 35 references that are in the map.
- First posted on arXiv 23 Sep 2025.

## Builds on (most cited first, 8 of 8)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [Self-Attention with Relative Position Representations](https://mlsanity.com/paper/1803.02155/) (2018)
- [Efficient Transformers: A Survey](https://mlsanity.com/paper/2009.06732/) (2020)
- [A Survey on Visual Transformer](https://mlsanity.com/paper/2012.12556/) (2020)
- [Residual Attention Network for Image Classification](https://mlsanity.com/paper/1704.06904/) (2017)

## Abstract (arXiv)

With the rapid development of society and continuous advances in science and technology, the food industry increasingly demands higher production quality and efficiency. Food image classification plays a vital role in enabling automated quality control on production lines, supporting food safety supervision, and promoting intelligent agricultural production. However, this task faces challenges due to the large number of parameters and high computational complexity of Vision Transformer models. To address these issues, we propose a lightweight food image classification algorithm that integrates a Window Multi-Head Attention Mechanism (WMHAM) and a Spatial Attention Mechanism (SAM). The WMHAM reduces computational cost by capturing local and global contextual features through efficient window partitioning, while the SAM adaptively emphasizes key spatial regions to improve discriminative feature representation. Experiments conducted on the Food-101 and Vireo Food-172 datasets demonstrate that our model achieves accuracies of 95.24% and 94.33%, respectively, while significantly reducing parameters and FLOPs compared with baseline methods. These results confirm that the proposed approach achieves an effective balance between computational efficiency and classification performance, making it well-suited for deployment in resource-constrained environments.