# Pixel-Level Change Detection Pseudo-Label Learning for Remote Sensing Change Captioning

arXiv: https://arxiv.org/abs/2312.15311 · HTML: https://mlsanity.com/paper/2312.15311/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 18 references that are in the map.
- First posted on arXiv 23 Dec 2023.

## Builds on (most cited first, 6 of 6)

- [Remote Sensing Image Change Detection with Transformers](https://mlsanity.com/paper/2103.00208/) (2021)
- [A Transformer-Based Siamese Network for Change Detection](https://mlsanity.com/paper/2201.01293/) (2022)
- [RSPrompter: Learning to Prompt for Remote Sensing Instance Segmentation based on Visual Foundation Model](https://mlsanity.com/paper/2306.16269/) (2023)
- [Describing and Localizing Multiple Changes with Transformers](https://mlsanity.com/paper/2103.14146/) (2021)
- [Changes to Captions: An Attentive Network for Remote Sensing Change Captioning](https://mlsanity.com/paper/2304.01091/) (2023)
- [Progressive Scale-aware Network for Remote sensing Image Change Captioning](https://mlsanity.com/paper/2303.00355/) (2023)

## Built on by (most cited first, 14 of 14)

- [Change-Agent: Towards Interactive Comprehensive Remote Sensing Change Interpretation and Analysis](https://mlsanity.com/paper/2403.19646/) (2024)
- [Text2Earth: Unlocking Text-driven Remote Sensing Image Generation with a Global-Scale Dataset and a Foundation Model](https://mlsanity.com/paper/2501.00895/) (2025)
- [Semantic-CC: Boosting Remote Sensing Image Change Captioning via Foundational Knowledge and Semantic Guidance](https://mlsanity.com/paper/2407.14032/) (2024)
- [Generalizable Disaster Damage Assessment via Change Detection with Vision Foundation Model](https://mlsanity.com/paper/2406.08020/) (2024)
- [MMM-RS: A Multi-modal, Multi-GSD, Multi-scene Remote Sensing Dataset and Benchmark for Text-to-Image Generation](https://mlsanity.com/paper/2410.22362/) (2024)
- [MV-CC: Mask Enhanced Video Model for Remote Sensing Change Caption](https://mlsanity.com/paper/2410.23946/) (2024)
- [Diffusion-RSCC: Diffusion Probabilistic Model for Change Captioning in Remote Sensing Images](https://mlsanity.com/paper/2405.12875/) (2024)
- [Efficient Semantic Splatting for Remote Sensing Multi-view Segmentation](https://mlsanity.com/paper/2412.05969/) (2024)
- [DFM: Difference Feature Modeling with Text-Guided Gated Contrastive Loss for Remote Sensing Image Change Captioning](https://mlsanity.com/paper/2606.27410/) (2026)
- [RSICCLLM: A Multimodal Large Language Model for Remote Sensing Image Change Captioning](https://mlsanity.com/paper/2606.28266/) (2026)
- [UAV as Urban Construction Change Monitor: A New Benchmark and Change Captioning Model](https://mlsanity.com/paper/2605.04409/) (2026)
- [HiSem: Hierarchical Semantic Disentangling for Remote Sensing Image Change Captioning](https://mlsanity.com/paper/2605.15024/) (2026)
- [LBTCap: A Lightweight Bilateral Transformer for Real-Time Remote Sensing Image Change Captioning](https://mlsanity.com/paper/2607.03320/) (2026)
- [Different Changes Require Different Reasoning: Change-Type-Specialized Experts for Robust Change Captioning](https://mlsanity.com/paper/2609.01136/) (2026)

## Abstract (arXiv)

The existing methods for Remote Sensing Image Change Captioning (RSICC) perform well in simple scenes but exhibit poorer performance in complex scenes. This limitation is primarily attributed to the model's constrained visual ability to distinguish and locate changes. Acknowledging the inherent correlation between change detection (CD) and RSICC tasks, we believe pixel-level CD is significant for describing the differences between images through language. Regrettably, the current RSICC dataset lacks readily available pixel-level CD labels. To address this deficiency, we leverage a model trained on existing CD datasets to derive CD pseudo-labels. We propose an innovative network with an auxiliary CD branch, supervised by pseudo-labels. Furthermore, a semantic fusion augment (SFA) module is proposed to fuse the feature information extracted by the CD branch, thereby facilitating the nuanced description of changes. Experiments demonstrate that our method achieves state-of-the-art performance and validate that learning pixel-level CD pseudo-labels significantly contributes to change captioning. Our code will be available at: https://github.com/Chen-Yang-Liu/Pix4Cap