# Change Detection Meets Visual Question Answering

arXiv: https://arxiv.org/abs/2112.06343 · HTML: https://mlsanity.com/paper/2112.06343/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 53 references that are in the map.
- First posted on arXiv 12 Dec 2021.

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

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [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)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://mlsanity.com/paper/2105.15203/) (2021)
- [VQA: Visual Question Answering](https://mlsanity.com/paper/1505.00468/) (2015)
- [Skip-Thought Vectors](https://mlsanity.com/paper/1506.06726/) (2015)
- [RSVQA: Visual Question Answering for Remote Sensing Data](https://mlsanity.com/paper/2003.07333/) (2020)
- [Exploring Models and Data for Remote Sensing Image Caption Generation](https://mlsanity.com/paper/1712.07835/) (2017)
- [Visual Storytelling](https://mlsanity.com/paper/1604.03968/) (2016)
- [Visual Question Answering: A Survey of Methods and Datasets](https://mlsanity.com/paper/1607.05910/) (2016)
- [Trends in Integration of Vision and Language Research: A Survey of Tasks, Datasets, and Methods](https://mlsanity.com/paper/1907.09358/) (2019)
- [Learning Spectral-Spatial-Temporal Features via a Recurrent Convolutional Neural Network for Change Detection in Multispectral Imagery](https://mlsanity.com/paper/1803.02642/) (2018)
- [Bi-Temporal Semantic Reasoning for the Semantic Change Detection in HR Remote Sensing Images](https://mlsanity.com/paper/2108.06103/) (2021)
- [Unsupervised Deep Slow Feature Analysis for Change Detection in Multi-Temporal Remote Sensing Images](https://mlsanity.com/paper/1812.00645/) (2018)
- [GETNET: A General End-to-end Two-dimensional CNN Framework for Hyperspectral Image Change Detection](https://mlsanity.com/paper/1905.01662/) (2019)
- [Self-supervised pre-training enhances change detection in Sentinel-2 imagery](https://mlsanity.com/paper/2101.08122/) (2021)
- [ASK: Adaptively Selecting Key Local Features for RGB-D Scene Recognition](https://mlsanity.com/paper/2110.07703/) (2021)
- [Learning Better Visual Dialog Agents with Pretrained Visual-Linguistic Representation](https://mlsanity.com/paper/2105.11541/) (2021)
- [How to find a good image-text embedding for remote sensing visual question answering?](https://mlsanity.com/paper/2109.11848/) (2021)

## Built on by (most cited first, 29 of 29)

- [RSGPT: A Remote Sensing Vision Language Model and Benchmark](https://mlsanity.com/paper/2307.15266/) (2023)
- [RSVG: Exploring Data and Models for Visual Grounding on Remote Sensing Data](https://mlsanity.com/paper/2210.12634/) (2022)
- [SkyEyeGPT: Unifying Remote Sensing Vision-Language Tasks via Instruction Tuning with Large Language Model](https://mlsanity.com/paper/2401.09712/) (2024)
- [Vision-Language Models in Remote Sensing: Current Progress and Future Trends](https://mlsanity.com/paper/2305.05726/) (2023)
- [TEOChat: A Large Vision-Language Assistant for Temporal Earth Observation Data](https://mlsanity.com/paper/2410.06234/) (2024)
- [RRSIS: Referring Remote Sensing Image Segmentation](https://mlsanity.com/paper/2306.08625/) (2023)
- [EarthNets: Empowering AI in Earth Observation](https://mlsanity.com/paper/2210.04936/) (2022)
- [Towards Vision-Language Geo-Foundation Model: A Survey](https://mlsanity.com/paper/2406.09385/) (2024)
- [On the Foundations of Earth and Climate Foundation Models](https://mlsanity.com/paper/2405.04285/) (2024)
- [Artificial intelligence to advance Earth observation: : A review of models, recent trends, and pathways forward](https://mlsanity.com/paper/2305.08413/) (2023)
- [From Image to Language: A Critical Analysis of Visual Question Answering (VQA) Approaches, Challenges, and Opportunities](https://mlsanity.com/paper/2311.00308/) (2023)
- [Parameter-Efficient Transfer Learning for Remote Sensing Image-Text Retrieval](https://mlsanity.com/paper/2308.12509/) (2023)
- [HRVQA: A Visual Question Answering Benchmark for High-Resolution Aerial Images](https://mlsanity.com/paper/2301.09460/) (2023)
- [DynamicVL: Benchmarking Multimodal Large Language Models for Dynamic City Understanding](https://mlsanity.com/paper/2505.21076/) (2025)
- [Show Me What and Where has Changed? Question Answering and Grounding for Remote Sensing Change Detection](https://mlsanity.com/paper/2410.23828/) (2024)
- [Multilingual Augmentation for Robust Visual Question Answering in Remote Sensing Images](https://mlsanity.com/paper/2304.03844/) (2023)
- [Text-Guided Coarse-to-Fine Fusion Network for Robust Remote Sensing Visual Question Answering](https://mlsanity.com/paper/2411.15770/) (2024)
- [EarthVL: A Progressive Earth Vision-Language Understanding and Generation Framework](https://mlsanity.com/paper/2601.02783/) (2026)
- [ChatENV: An Interactive Vision-Language Model for Sensor-Guided Environmental Monitoring and Scenario Simulation](https://mlsanity.com/paper/2508.10635/) (2025)
- [AirSpatialBot: A Spatially-Aware Aerial Agent for Fine-Grained Vehicle Attribute Recognization and Retrieval](https://mlsanity.com/paper/2601.01416/) (2026)
- [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)
- [UAV traffic scene understanding: A regulation embedded multi-modal network and a unified benchmark](https://mlsanity.com/paper/2603.10722/) (2026)
- [Revisiting Change VQA in Remote Sensing with Structured and Native Multimodal Qwen Models](https://mlsanity.com/paper/2604.18429/) (2026)
- [Copy-Move Forgery Detection and Question Answering for Remote Sensing Image](https://mlsanity.com/paper/2412.02575/) (2024)
- [Improving Few-Shot Change Detection Visual Question Answering via Decision-Ambiguity-guided Reinforcement Fine-Tuning](https://mlsanity.com/paper/2512.24591/) (2025)
- [A High-Level Survey of Optical Remote Sensing](https://mlsanity.com/paper/2602.17397/) (2026)
- [JL1-CC&QA: Extending the JL1-CD Benchmark with Change Captioning and Question Answering](https://mlsanity.com/paper/2606.31745/) (2026)
- [Selective Tool Use for Agentic Change Visual Question Answering in Remote Sensing](https://mlsanity.com/paper/2609.14523/) (2026)

## Abstract (arXiv)

The Earth's surface is continually changing, and identifying changes plays an important role in urban planning and sustainability. Although change detection techniques have been successfully developed for many years, these techniques are still limited to experts and facilitators in related fields. In order to provide every user with flexible access to change information and help them better understand land-cover changes, we introduce a novel task: change detection-based visual question answering (CDVQA) on multi-temporal aerial images. In particular, multi-temporal images can be queried to obtain high level change-based information according to content changes between two input images. We first build a CDVQA dataset including multi-temporal image-question-answer triplets using an automatic question-answer generation method. Then, a baseline CDVQA framework is devised in this work, and it contains four parts: multi-temporal feature encoding, multi-temporal fusion, multi-modal fusion, and answer prediction. In addition, we also introduce a change enhancing module to multi-temporal feature encoding, aiming at incorporating more change-related information. Finally, effects of different backbones and multi-temporal fusion strategies are studied on the performance of CDVQA task. The experimental results provide useful insights for developing better CDVQA models, which are important for future research on this task.