# SEMT: Static-Expansion-Mesh Transformer Network Architecture for Remote Sensing Image Captioning

arXiv: https://arxiv.org/abs/2507.12845 · HTML: https://mlsanity.com/paper/2507.12845/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 30 references that are in the map.
- First posted on arXiv 17 Jul 2025.

## Builds on (most cited first, 9 of 9)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Meshed-Memory Transformer for Image Captioning](https://mlsanity.com/paper/1912.08226/) (2019)
- [Object Detection in Optical Remote Sensing Images: A Survey and A New Benchmark](https://mlsanity.com/paper/1909.00133/) (2019)
- [Advancing Plain Vision Transformer Towards Remote Sensing Foundation Model](https://mlsanity.com/paper/2208.03987/) (2022)
- [Large Language Models for Captioning and Retrieving Remote Sensing Images](https://mlsanity.com/paper/2402.06475/) (2024)
- [Exploiting Multiple Sequence Lengths in Fast End to End Training for Image Captioning](https://mlsanity.com/paper/2208.06551/) (2022)
- [A Light-weight Deep Learning Model for Remote Sensing Image Classification](https://mlsanity.com/paper/2302.13028/) (2023)
- [A Robust and Low Complexity Deep Learning Model for Remote Sensing Image Classification](https://mlsanity.com/paper/2211.02820/) (2022)
- [Instance-aware Remote Sensing Image Captioning with Cross-hierarchy Attention](https://mlsanity.com/paper/2105.04996/) (2021)

## Abstract (arXiv)

Image captioning has emerged as a crucial task in the intersection of computer vision and natural language processing, enabling automated generation of descriptive text from visual content. In the context of remote sensing, image captioning plays a significant role in interpreting vast and complex satellite imagery, aiding applications such as environmental monitoring, disaster assessment, and urban planning. This motivates us, in this paper, to present a transformer based network architecture for remote sensing image captioning (RSIC) in which multiple techniques of Static Expansion, Memory-Augmented Self-Attention, Mesh Transformer are evaluated and integrated. We evaluate our proposed models using two benchmark remote sensing image datasets of UCM-Caption and NWPU-Caption. Our best model outperforms the state-of-the-art systems on most of evaluation metrics, which demonstrates potential to apply for real-life remote sensing image systems.