# Multilingual Augmentation for Robust Visual Question Answering in Remote Sensing Images

arXiv: https://arxiv.org/abs/2304.03844 · HTML: https://mlsanity.com/paper/2304.03844/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 16 references that are in the map.
- First posted on arXiv 7 Apr 2023.

## Builds on (most cited first, 11 of 11)

- [VQA: Visual Question Answering](https://mlsanity.com/paper/1505.00468/) (2015)
- [Cross-Modal Self-Attention Network for Referring Image Segmentation](https://mlsanity.com/paper/1904.04745/) (2019)
- [BigEarthNet: A Large-Scale Benchmark Archive For Remote Sensing Image Understanding](https://mlsanity.com/paper/1902.06148/) (2019)
- [RSVQA: Visual Question Answering for Remote Sensing Data](https://mlsanity.com/paper/2003.07333/) (2020)
- [EarthNets: Empowering AI in Earth Observation](https://mlsanity.com/paper/2210.04936/) (2022)
- [From Easy to Hard: Learning Language-guided Curriculum for Visual Question Answering on Remote Sensing Data](https://mlsanity.com/paper/2205.03147/) (2022)
- [GETNET: A General End-to-end Two-dimensional CNN Framework for Hyperspectral Image Change Detection](https://mlsanity.com/paper/1905.01662/) (2019)
- [Change Detection Meets Visual Question Answering](https://mlsanity.com/paper/2112.06343/) (2021)
- [SD-RSIC: Summarization Driven Deep Remote Sensing Image Captioning](https://mlsanity.com/paper/2006.08432/) (2020)
- [How to find a good image-text embedding for remote sensing visual question answering?](https://mlsanity.com/paper/2109.11848/) (2021)
- [Disentangled Latent Transformer for Interpretable Monocular Height Estimation](https://mlsanity.com/paper/2201.06357/) (2022)

## Built on by (most cited first, 7 of 7)

- [Vision-Language Models in Remote Sensing: Current Progress and Future Trends](https://mlsanity.com/paper/2305.05726/) (2023)
- [RRSIS: Referring Remote Sensing Image Segmentation](https://mlsanity.com/paper/2306.08625/) (2023)
- [Visual Question Answering on Multiple Remote Sensing Image Modalities](https://mlsanity.com/paper/2505.15401/) (2025)
- [SAR Strikes Back: A New Hope for RSVQA](https://mlsanity.com/paper/2501.08131/) (2025)
- [Checkmate: interpretable and explainable RSVQA is the endgame](https://mlsanity.com/paper/2508.13086/) (2025)
- [Overcoming Language Bias in Remote Sensing Visual Question Answering via Adversarial Training](https://mlsanity.com/paper/2306.00483/) (2023)
- [Text-conditioned State Space Model For Domain-generalized Change Detection Visual Question Answering](https://mlsanity.com/paper/2508.08974/) (2025)

## Abstract (arXiv)

Aiming at answering questions based on the content of remotely sensed images, visual question answering for remote sensing data (RSVQA) has attracted much attention nowadays. However, previous works in RSVQA have focused little on the robustness of RSVQA. As we aim to enhance the reliability of RSVQA models, how to learn robust representations against new words and different question templates with the same meaning is the key challenge. With the proposed augmented dataset, we are able to obtain more questions in addition to the original ones with the same meaning. To make better use of this information, in this study, we propose a contrastive learning strategy for training robust RSVQA models against diverse question templates and words. Experimental results demonstrate that the proposed augmented dataset is effective in improving the robustness of the RSVQA model. In addition, the contrastive learning strategy performs well on the low resolution (LR) dataset.