# Changes to Captions: An Attentive Network for Remote Sensing Change Captioning

arXiv: https://arxiv.org/abs/2304.01091 · HTML: https://mlsanity.com/paper/2304.01091/

## Key facts

- Cited by 41 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 55 references that are in the map.
- First posted on arXiv 3 Apr 2023.

## Builds on (most cited first, 23 of 23)

- [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)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [CIDEr: Consensus-based Image Description Evaluation](https://mlsanity.com/paper/1411.5726/) (2014)
- [Show, Attend and Tell: Neural Image Caption Generation with Visual Attention](https://mlsanity.com/paper/1502.03044/) (2015)
- [Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering](https://mlsanity.com/paper/1707.07998/) (2017)
- [Stacked Cross Attention for Image-Text Matching](https://mlsanity.com/paper/1803.08024/) (2018)
- [Attention on Attention for Image Captioning](https://mlsanity.com/paper/1908.06954/) (2019)
- [Knowing When to Look: Adaptive Attention via A Visual Sentinel for Image Captioning](https://mlsanity.com/paper/1612.01887/) (2016)
- [Exploring Models and Data for Remote Sensing Image Caption Generation](https://mlsanity.com/paper/1712.07835/) (2017)
- [From Show to Tell: A Survey on Deep Learning-based Image Captioning](https://mlsanity.com/paper/2107.06912/) (2021)
- [Learning to Describe Differences Between Pairs of Similar Images](https://mlsanity.com/paper/1808.10584/) (2018)
- [Describing and Localizing Multiple Changes with Transformers](https://mlsanity.com/paper/2103.14146/) (2021)
- [Image Difference Captioning with Pre-training and Contrastive Learning](https://mlsanity.com/paper/2202.04298/) (2022)
- [Txt2Img-MHN: Remote Sensing Image Generation from Text Using Modern Hopfield Networks](https://mlsanity.com/paper/2208.04441/) (2022)
- [Progressive Scale-aware Network for Remote sensing Image Change Captioning](https://mlsanity.com/paper/2303.00355/) (2023)
- [Finding It at Another Side: A Viewpoint-Adapted Matching Encoder for Change Captioning](https://mlsanity.com/paper/2009.14352/) (2020)
- [CLIP4IDC: CLIP for Image Difference Captioning](https://mlsanity.com/paper/2206.00629/) (2022)
- [Neighborhood Contrastive Transformer for Change Captioning](https://mlsanity.com/paper/2303.03171/) (2023)
- [Multi-Temporal Scene Classification and Scene Change Detection with Correlation based Fusion](https://mlsanity.com/paper/2006.02176/) (2020)
- [Consistency-Regularized Region-Growing Network for Semantic Segmentation of Urban Scenes with Point-Level Annotations](https://mlsanity.com/paper/2202.03740/) (2022)
- [HAR-Net: Joint Learning of Hybrid Attention for Single-stage Object Detection](https://mlsanity.com/paper/1904.11141/) (2019)

## Built on by (most cited first, 30 of 41)

- [Change-Agent: Towards Interactive Comprehensive Remote Sensing Change Interpretation and Analysis](https://mlsanity.com/paper/2403.19646/) (2024)
- [Describing Differences in Image Sets with Natural Language](https://mlsanity.com/paper/2312.02974/) (2023)
- [SpectralMamba: Efficient Mamba for Hyperspectral Image Classification](https://mlsanity.com/paper/2404.08489/) (2024)
- [RSCaMa: Remote Sensing Image Change Captioning with State Space Model](https://mlsanity.com/paper/2404.18895/) (2024)
- [Semantic-CC: Boosting Remote Sensing Image Change Captioning via Foundational Knowledge and Semantic Guidance](https://mlsanity.com/paper/2407.14032/) (2024)
- [Enhancing Perception of Key Changes in Remote Sensing Image Change Captioning](https://mlsanity.com/paper/2409.12612/) (2024)
- [Pixel-Level Change Detection Pseudo-Label Learning for Remote Sensing Change Captioning](https://mlsanity.com/paper/2312.15311/) (2023)
- [RSCC: A Large-Scale Remote Sensing Change Caption Dataset for Disaster Events](https://mlsanity.com/paper/2509.01907/) (2025)
- [A Lightweight Sparse Focus Transformer for Remote Sensing Image Change Captioning](https://mlsanity.com/paper/2405.06598/) (2024)
- [RSAdapter: Adapting Multimodal Models for Remote Sensing Visual Question Answering](https://mlsanity.com/paper/2310.13120/) (2023)
- [Mask Approximation Net: A Novel Diffusion Model Approach for Remote Sensing Change Captioning](https://mlsanity.com/paper/2412.19179/) (2024)
- [Context-aware Difference Distilling for Multi-change Captioning](https://mlsanity.com/paper/2405.20810/) (2024)
- [MaskCD: A Remote Sensing Change Detection Network Based on Mask Classification](https://mlsanity.com/paper/2404.12081/) (2024)
- [Robust Change Captioning in Remote Sensing: SECOND-CC Dataset and MModalCC Framework](https://mlsanity.com/paper/2501.10075/) (2025)
- [Dual Selective Fusion Transformer Network for Hyperspectral Image Classification](https://mlsanity.com/paper/2410.03171/) (2024)
- [ChangeMinds: Multi-task Framework for Detecting and Describing Changes in Remote Sensing](https://mlsanity.com/paper/2410.10047/) (2024)
- [BTCChat: Advancing Remote Sensing Bi-temporal Change Captioning with Multimodal Large Language Model](https://mlsanity.com/paper/2509.05895/) (2025)
- [Towards a multimodal framework for remote sensing image change retrieval and captioning](https://mlsanity.com/paper/2406.13424/) (2024)
- [DescribeEarth: Describe Anything for Remote Sensing Images](https://mlsanity.com/paper/2509.25654/) (2025)
- [AD-Copilot: A Vision-Language Assistant for Industrial Anomaly Detection via Visual In-context Comparison](https://mlsanity.com/paper/2603.13779/) (2026)
- [Viewpoint Integration and Registration with Vision Language Foundation Model for Image Change Understanding](https://mlsanity.com/paper/2309.08585/) (2023)
- [MV-CC: Mask Enhanced Video Model for Remote Sensing Change Caption](https://mlsanity.com/paper/2410.23946/) (2024)
- [SAM Guided Semantic and Motion Changed Region Mining for Remote Sensing Change Captioning](https://mlsanity.com/paper/2511.21420/) (2025)
- [Diffusion-RSCC: Diffusion Probabilistic Model for Change Captioning in Remote Sensing Images](https://mlsanity.com/paper/2405.12875/) (2024)
- [Visual Chronicles: Using Multimodal LLMs to Analyze Massive Collections of Images](https://mlsanity.com/paper/2504.08727/) (2025)
- [Deciphering Personalization: Towards Fine-Grained Explainability in Natural Language for Personalized Image Generation Models](https://mlsanity.com/paper/2511.01932/) (2025)
- [UAV as Urban Construction Change Monitor: A New Benchmark and Change Captioning Model](https://mlsanity.com/paper/2605.04409/) (2026)
- [EchoChange: A Diffusion Language Model with Dual Pass Remasking for Factual Remote Sensing Disaster Change Captioning](https://mlsanity.com/paper/2608.01856/) (2026)
- [Can Deep Network Balance Copy-Move Forgery Detection and Distinguishment?](https://mlsanity.com/paper/2305.10247/) (2023)
- [ImageSet2Text: Describing Sets of Images through Text](https://mlsanity.com/paper/2503.19361/) (2025)

## Abstract (arXiv)

In recent years, advanced research has focused on the direct learning and analysis of remote sensing images using natural language processing (NLP) techniques. The ability to accurately describe changes occurring in multi-temporal remote sensing images is becoming increasingly important for geospatial understanding and land planning. Unlike natural image change captioning tasks, remote sensing change captioning aims to capture the most significant changes, irrespective of various influential factors such as illumination, seasonal effects, and complex land covers. In this study, we highlight the significance of accurately describing changes in remote sensing images and present a comparison of the change captioning task for natural and synthetic images and remote sensing images. To address the challenge of generating accurate captions, we propose an attentive changes-to-captions network, called Chg2Cap for short, for bi-temporal remote sensing images. The network comprises three main components: 1) a Siamese CNN-based feature extractor to collect high-level representations for each image pair; 2) an attentive decoder that includes a hierarchical self-attention block to locate change-related features and a residual block to generate the image embedding; and 3) a transformer-based caption generator to decode the relationship between the image embedding and the word embedding into a description. The proposed Chg2Cap network is evaluated on two representative remote sensing datasets, and a comprehensive experimental analysis is provided. The code and pre-trained models will be available online at https://github.com/ShizhenChang/Chg2Cap.